Persistent network model diagnostics - balanced statistics
This file shows diagnostics for persistent network models fit using balanced racial/ethnic mixing matrices and degree terms adjusted to correspond to the balanced race/ethnicity mixing matrices. In addition to adjusting race-specific degree, we adjusted the regional degree to be the weighted average of race/ethnicity-specific degrees. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.
Load packages and model fits
rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")## Loading required package: lattice## Loading required package: RColorBrewerlibrary("knitr")
library("kableExtra")
library("here")## here() starts at /homes/dpwhite/R/GitHub Repos/WHAMPload(file = here("Model fits and simulations/Fit tests and debugging/est/fit.p.buildup.bal.rda"))Model terms and control settings
| Terms | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
|---|---|---|---|---|---|---|---|---|
| edges | 2017.5 | 2017.5 | 2017.5 | 2017.5 | 2017.5 | 2017.5 | 2017.5 | 2017.5 |
| nodefactor.deg.main.1 | NA | NA | NA | 1699.0 | 1699.0 | 1699.0 | 1699.0 | 1699.0 |
| nodefactor.race..wa.B | NA | 285.5 | 285.5 | 285.5 | 285.5 | 285.5 | 285.5 | 285.5 |
| nodefactor.race..wa.H | NA | 605.3 | 605.3 | 605.3 | 605.3 | 605.3 | 605.3 | 605.3 |
| nodefactor.region.EW | NA | NA | NA | NA | 424.5 | 424.5 | 424.5 | 424.5 |
| nodefactor.region.OW | NA | NA | NA | NA | 1312.6 | 1312.6 | 1312.6 | 1312.6 |
| concurrent | NA | NA | NA | NA | NA | NA | 1384.0 | 1384.0 |
| nodematch.race..wa.B | NA | NA | 8.5 | 8.5 | 8.5 | 8.5 | 8.5 | 8.5 |
| nodematch.race..wa.H | NA | NA | 51.2 | 51.2 | 51.2 | 51.2 | 51.2 | 51.2 |
| nodematch.race..wa.O | NA | NA | 1247.1 | 1247.1 | 1247.1 | 1247.1 | 1247.1 | 1247.1 |
| nodematch.region | NA | NA | NA | NA | NA | NA | NA | 1614.0 |
| absdiff.sqrt.age | NA | NA | NA | NA | NA | 1664.8 | 1664.8 | 1664.8 |
| degrange | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| nodematch.role.class.I | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
| nodematch.role.class.R | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
The control settings for these models are:
set.control.ergm = ccontrol.ergm(MCMC.interval = 1e+5,
MCMC.samplesize = 7500,
MCMC.burnin = 1e+6,
MPLE.max.dyad.types = 1e+7,
init.method = "zeros",
MCMLE.maxit = 400,
parallel = np/2,
parallel.type="PSOCK"))MCMC diagnostics
Model 1
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## -0.8468 40.4556 0.2336 0.2328
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## -79.5 -28.5 -0.5 26.5 79.5
##
##
## Sample statistics cross-correlations:
## edges
## edges 1
##
## Sample statistics auto-correlation:
## Chain 1
## edges
## Lag 0 1.00000000
## Lag 1e+05 -0.02696659
## Lag 2e+05 -0.02004393
## Lag 3e+05 0.01311327
## Lag 4e+05 0.00778886
## Lag 5e+05 -0.01596866
## Chain 2
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.007778250
## Lag 2e+05 0.002992239
## Lag 3e+05 -0.030414501
## Lag 4e+05 0.022742004
## Lag 5e+05 -0.007225642
## Chain 3
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.008358496
## Lag 2e+05 0.005713489
## Lag 3e+05 0.001082667
## Lag 4e+05 0.003699070
## Lag 5e+05 -0.012638623
## Chain 4
## edges
## Lag 0 1.00000000
## Lag 1e+05 -0.02196361
## Lag 2e+05 0.01836192
## Lag 3e+05 -0.01450819
## Lag 4e+05 -0.02158209
## Lag 5e+05 0.01242602
## Chain 5
## edges
## Lag 0 1.000000e+00
## Lag 1e+05 9.814999e-03
## Lag 2e+05 3.249351e-03
## Lag 3e+05 -1.136881e-02
## Lag 4e+05 2.727052e-02
## Lag 5e+05 -6.478774e-05
## Chain 6
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.014358682
## Lag 2e+05 -0.001337833
## Lag 3e+05 0.003429987
## Lag 4e+05 0.013270079
## Lag 5e+05 -0.002446943
## Chain 7
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.002834492
## Lag 2e+05 0.017856358
## Lag 3e+05 0.025845842
## Lag 4e+05 -0.006661281
## Lag 5e+05 0.013340348
## Chain 8
## edges
## Lag 0 1.0000000000
## Lag 1e+05 0.0070005526
## Lag 2e+05 0.0130928264
## Lag 3e+05 0.0005163194
## Lag 4e+05 -0.0034014384
## Lag 5e+05 -0.0190616365
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.07428
##
## Individual P-values (lower = worse):
## edges
## 0.9407842
## Joint P-value (lower = worse): 0.9443706 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.1574
##
## Individual P-values (lower = worse):
## edges
## 0.8749107
## Joint P-value (lower = worse): 0.8743597 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.4119
##
## Individual P-values (lower = worse):
## edges
## 0.6804084
## Joint P-value (lower = worse): 0.6769639 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.5496
##
## Individual P-values (lower = worse):
## edges
## 0.582616
## Joint P-value (lower = worse): 0.5714885 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.6912
##
## Individual P-values (lower = worse):
## edges
## 0.4894154
## Joint P-value (lower = worse): 0.4917554 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.2149
##
## Individual P-values (lower = worse):
## edges
## 0.8298477
## Joint P-value (lower = worse): 0.8316306 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -1.084
##
## Individual P-values (lower = worse):
## edges
## 0.2785084
## Joint P-value (lower = worse): 0.304045 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.8128
##
## Individual P-values (lower = worse):
## edges
## 0.4163452
## Joint P-value (lower = worse): 0.431546 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 2
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 2.7829 40.49 0.23377 0.23443
## nodefactor.race..wa.B 0.4447 16.19 0.09348 0.09421
## nodefactor.race..wa.H 0.1001 23.63 0.13642 0.14044
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -76.50 -24.50 2.5000 29.50 82.50
## nodefactor.race..wa.B -30.52 -10.52 0.4832 11.48 32.48
## nodefactor.race..wa.H -45.34 -16.34 -0.3400 15.66 46.66
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.34641019
## nodefactor.race..wa.B 0.3464102 1.00000000
## nodefactor.race..wa.H 0.4751787 0.07918742
## nodefactor.race..wa.H
## edges 0.47517872
## nodefactor.race..wa.B 0.07918742
## nodefactor.race..wa.H 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.023232748 -0.0034959916 0.017875514
## Lag 2e+05 -0.001983736 -0.0168220279 0.008358084
## Lag 3e+05 -0.015795757 0.0003010341 0.014590133
## Lag 4e+05 0.001802848 0.0020808864 0.004937218
## Lag 5e+05 0.019669158 -0.0173034648 -0.001465206
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0045421940 -0.018095630 0.025873746
## Lag 2e+05 -0.0145265213 -0.017655310 0.024357123
## Lag 3e+05 -0.0009146192 -0.007574214 -0.005346450
## Lag 4e+05 0.0116077823 -0.012094749 0.011126297
## Lag 5e+05 0.0163075780 0.006546645 0.008242495
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.003414915 0.02665128 0.005827806
## Lag 2e+05 0.025989936 0.02232682 -0.003180250
## Lag 3e+05 0.012735473 0.01097319 0.006196804
## Lag 4e+05 0.016153042 -0.01542873 0.004917451
## Lag 5e+05 -0.006425709 -0.01618662 0.024526624
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.001585530 -0.011137271 0.028450240
## Lag 2e+05 0.012156279 0.033192932 0.037780174
## Lag 3e+05 -0.000473927 -0.005939590 -0.001885223
## Lag 4e+05 -0.010783129 -0.001187619 -0.028444440
## Lag 5e+05 -0.018093074 -0.009314924 -0.035647671
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.005190244 -0.001857585 0.038500361
## Lag 2e+05 0.015650195 -0.002226692 -0.003686892
## Lag 3e+05 -0.006203796 -0.001689881 0.010046951
## Lag 4e+05 0.008492773 -0.012492375 0.008159489
## Lag 5e+05 0.022130337 0.002603665 0.002554883
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.002224462 0.002457265 -0.008035761
## Lag 2e+05 0.019293265 0.010460472 0.007574993
## Lag 3e+05 0.026542859 -0.004913981 -0.001679060
## Lag 4e+05 -0.021616873 0.004677633 -0.028355282
## Lag 5e+05 -0.018191063 -0.002328437 0.042905056
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 0.0080093855 0.0255715296 0.024469357
## Lag 2e+05 0.0063013776 0.0003394649 -0.007383481
## Lag 3e+05 -0.0204270038 -0.0095750156 0.009527346
## Lag 4e+05 0.0005862355 0.0231250718 -0.040485758
## Lag 5e+05 0.0190976392 -0.0137702329 0.009395955
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.018646315 0.017610496 -0.011327738
## Lag 2e+05 0.005205172 -0.008957189 0.044683441
## Lag 3e+05 0.017896133 -0.021864442 0.009138015
## Lag 4e+05 -0.006663206 -0.009502223 0.032640846
## Lag 5e+05 -0.015510566 -0.008426728 -0.003306575
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.1531 0.7950 -0.3341
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8782844 0.4266154 0.7383364
## Joint P-value (lower = worse): 0.8157513 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.6462 -1.6511 -0.2238
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.51814860 0.09870959 0.82289505
## Joint P-value (lower = worse): 0.5485255 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.5895 1.2290 0.4294
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.5554960 0.2190740 0.6676638
## Joint P-value (lower = worse): 0.3035522 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1531 -0.4291 1.2294
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8783140 0.6678485 0.2189118
## Joint P-value (lower = worse): 0.5504082 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1770 -0.2772 0.6326
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8594813 0.7816439 0.5269760
## Joint P-value (lower = worse): 0.9310504 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.3689 0.1673 1.1248
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1710220 0.8671699 0.2606925
## Joint P-value (lower = worse): 0.4331449 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.1796 0.5687 1.2053
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8574286 0.5695450 0.2280723
## Joint P-value (lower = worse): 0.3901039 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8903 1.0652 0.8932
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3732863 0.2867882 0.3717392
## Joint P-value (lower = worse): 0.5993255 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 3
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 1.5222667 40.106 0.23155 0.23381
## nodefactor.race..wa.B 0.0002333 16.100 0.09295 0.09293
## nodefactor.race..wa.H 0.2496333 23.714 0.13691 0.13681
## nodematch.race..wa.B 0.0009844 2.877 0.01661 0.01669
## nodematch.race..wa.H 0.0717697 6.957 0.04016 0.04017
## nodematch.race..wa.O 1.2760540 32.645 0.18848 0.18977
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -77.50 -25.500 1.5000 28.500 80.50
## nodefactor.race..wa.B -31.52 -10.517 -0.5168 10.483 31.48
## nodefactor.race..wa.H -46.34 -15.340 -0.3400 16.660 46.66
## nodematch.race..wa.B -5.48 -2.480 -0.4798 1.520 5.52
## nodematch.race..wa.H -13.18 -4.181 -0.1815 4.819 13.82
## nodematch.race..wa.O -62.08 -21.081 0.9192 23.919 65.92
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.00000000 0.344861890
## nodefactor.race..wa.B 0.34486189 1.000000000
## nodefactor.race..wa.H 0.47506781 0.112449321
## nodematch.race..wa.B 0.05763626 0.318548319
## nodematch.race..wa.H 0.12962301 -0.002223137
## nodematch.race..wa.O 0.78300875 -0.023830062
## nodefactor.race..wa.H nodematch.race..wa.B
## edges 0.475067813 0.057636263
## nodefactor.race..wa.B 0.112449321 0.318548319
## nodefactor.race..wa.H 1.000000000 -0.001642114
## nodematch.race..wa.B -0.001642114 1.000000000
## nodematch.race..wa.H 0.496849030 -0.003865249
## nodematch.race..wa.O -0.029537128 -0.002310991
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.129623012 0.783008750
## nodefactor.race..wa.B -0.002223137 -0.023830062
## nodefactor.race..wa.H 0.496849030 -0.029537128
## nodematch.race..wa.B -0.003865249 -0.002310991
## nodematch.race..wa.H 1.000000000 0.006342064
## nodematch.race..wa.O 0.006342064 1.000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.015843825 0.004476658 -0.0123840990
## Lag 2e+05 0.022830608 0.026081597 -0.0004758322
## Lag 3e+05 -0.010266978 0.022497876 -0.0074809756
## Lag 4e+05 -0.010176100 0.007010111 0.0027888468
## Lag 5e+05 0.001646553 -0.003565484 -0.0059172417
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.029507659 0.0044240860 0.011697669
## Lag 2e+05 0.029280846 -0.0002156142 0.030530143
## Lag 3e+05 -0.017892476 0.0078809289 -0.029823488
## Lag 4e+05 -0.019757221 -0.0013986078 0.002205789
## Lag 5e+05 -0.002560023 0.0041369884 0.008078494
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.016941891 0.019706497 0.03637509
## Lag 2e+05 -0.005247894 0.038019267 0.01483554
## Lag 3e+05 0.004445855 0.008358530 -0.02340338
## Lag 4e+05 0.002285735 -0.007735001 -0.03433566
## Lag 5e+05 -0.026065446 -0.012895606 -0.01203539
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.005197915 0.021035681 0.014982553
## Lag 2e+05 -0.032404844 0.002873927 -0.020067088
## Lag 3e+05 0.008607397 -0.019044091 0.005422555
## Lag 4e+05 -0.001680810 0.010554109 0.014134414
## Lag 5e+05 -0.007416965 0.003038953 -0.037716053
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.013824569 0.02447254 -0.006410762
## Lag 2e+05 0.012501892 0.01085864 -0.022218053
## Lag 3e+05 -0.005301712 -0.01740529 0.008131581
## Lag 4e+05 -0.003091473 -0.01582085 -0.011415356
## Lag 5e+05 -0.008102035 -0.03086505 -0.016850482
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.004759781 0.014603155 -0.004154625
## Lag 2e+05 -0.014105057 -0.010864395 -0.012818269
## Lag 3e+05 -0.012762842 -0.002703005 0.008316462
## Lag 4e+05 -0.011327110 0.006003129 -0.017629108
## Lag 5e+05 -0.020346747 -0.011706623 -0.006805634
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.045313579 -0.025116085 0.005863421
## Lag 2e+05 -0.010907397 -0.007486236 -0.001959731
## Lag 3e+05 0.018231035 0.013221813 -0.006372782
## Lag 4e+05 -0.001195779 -0.028200413 0.004864977
## Lag 5e+05 -0.016160695 0.009750146 -0.001770768
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.018438565 -0.013820120 0.054690503
## Lag 2e+05 -0.006101967 -0.011768178 0.004726460
## Lag 3e+05 0.002448923 -0.009652485 0.001804332
## Lag 4e+05 -0.012426713 0.011274481 0.008135527
## Lag 5e+05 0.026216660 -0.008733913 -0.021894075
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.012817636 -0.005102629 -0.002837681
## Lag 2e+05 0.045322769 0.010927296 0.016531982
## Lag 3e+05 -0.028163274 -0.038111845 0.011669294
## Lag 4e+05 0.007378126 -0.007475069 -0.015207659
## Lag 5e+05 -0.005340183 0.005197425 -0.021014110
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.012165410 -0.004399127 0.03075894
## Lag 2e+05 0.017837163 -0.003784671 0.01654131
## Lag 3e+05 0.020784562 0.018849655 -0.01602348
## Lag 4e+05 0.009287822 -0.018791366 -0.02407606
## Lag 5e+05 0.001040370 0.012324844 -0.02454469
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0067476866 -0.009602250 -0.008286705
## Lag 2e+05 0.0006613100 -0.019757738 -0.005420505
## Lag 3e+05 -0.0005028809 0.024108199 0.011812553
## Lag 4e+05 -0.0009809733 0.006131602 0.014168785
## Lag 5e+05 0.0029458956 0.003573553 0.007827066
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.011395498 0.001106497 0.0198841455
## Lag 2e+05 0.006682362 0.008362255 0.0077796532
## Lag 3e+05 -0.017115036 0.001273001 -0.0002402171
## Lag 4e+05 0.016912910 0.010364218 -0.0021866985
## Lag 5e+05 -0.014951882 -0.030688079 -0.0275065489
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.001787205 0.002876734 0.005453866
## Lag 2e+05 0.004834070 0.025302369 0.006328637
## Lag 3e+05 -0.010475370 -0.014976960 0.020387675
## Lag 4e+05 0.011461063 0.029679329 0.014062944
## Lag 5e+05 0.016661217 0.005946060 0.009176090
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.0000000000
## Lag 1e+05 -0.02562748 0.001547637 -0.0092781256
## Lag 2e+05 0.03018740 -0.005831895 -0.0026172231
## Lag 3e+05 -0.01769355 0.021112238 -0.0391936562
## Lag 4e+05 -0.01323315 0.006628095 0.0003841655
## Lag 5e+05 -0.00647332 0.030100880 0.0269388531
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.004602997 -0.006864796 0.002524391
## Lag 2e+05 0.014423581 0.005204988 0.020539203
## Lag 3e+05 -0.007827058 0.014588442 0.004986211
## Lag 4e+05 -0.015182835 0.010855256 -0.020622392
## Lag 5e+05 -0.023647507 -0.021803824 0.011741422
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.009783396 -0.0078549289 0.014854467
## Lag 2e+05 0.012242667 0.0191136821 0.003589613
## Lag 3e+05 0.004797528 -0.0003533467 -0.005882155
## Lag 4e+05 0.017046128 0.0163171595 0.002185058
## Lag 5e+05 -0.024226989 0.0073939906 -0.018867042
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.0586 0.8460 0.2044
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.5267 -2.0277 0.9819
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.28979366 0.39756485 0.83802019
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.59839016 0.04258782 0.32616319
## Joint P-value (lower = worse): 0.1025072 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.8268 0.6703 0.2626
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.2348 -0.3483 -1.2736
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4083639 0.5026756 0.7928784
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8144008 0.7276253 0.2028033
## Joint P-value (lower = worse): 0.7204317 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.436 1.212 -1.786
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.916 -1.288 -1.349
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.15110888 0.22543588 0.07416461
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.05534429 0.19778952 0.17744405
## Joint P-value (lower = worse): 0.2219073 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.24162 -0.84516 -0.01459
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.70256 -1.02005 1.61859
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.21437684 0.39802206 0.98835719
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.08865069 0.30770394 0.10553590
## Joint P-value (lower = worse): 0.3567958 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7007 -0.7338 -0.5113
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.2605 -0.3475 1.6395
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4834803 0.4630604 0.6091362
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.7944457 0.7281874 0.1011013
## Joint P-value (lower = worse): 0.8505518 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.01658 -0.06448 -0.02851
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.80942 1.03286 0.18146
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9867695 0.9485907 0.9772553
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.4182714 0.3016712 0.8560040
## Joint P-value (lower = worse): 0.881198 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.73041 -1.98808 0.09989
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.48089 0.44096 -1.43276
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.08355794 0.04680232 0.92043056
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.63059246 0.65923957 0.15192573
## Joint P-value (lower = worse): 0.3536858 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.8216 2.2475 0.2049
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.4535 0.1956 1.1446
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.06851677 0.02460811 0.83766066
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.65016843 0.84494180 0.25239195
## Joint P-value (lower = worse): 0.2052411 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 4
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.15977 39.966 0.23074 0.23260
## nodefactor.deg.main.1 -0.20630 45.345 0.26180 0.26047
## nodefactor.race..wa.B 0.03713 16.053 0.09268 0.09411
## nodefactor.race..wa.H 0.14393 23.553 0.13598 0.13686
## nodematch.race..wa.B -0.05322 2.884 0.01665 0.01623
## nodematch.race..wa.H 0.06987 6.975 0.04027 0.04013
## nodematch.race..wa.O 0.11812 32.665 0.18859 0.18896
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -78.50 -26.500 0.50000 27.500 78.50
## nodefactor.deg.main.1 -89.00 -31.000 0.00000 30.000 89.00
## nodefactor.race..wa.B -30.52 -10.517 -0.51680 10.483 32.48
## nodefactor.race..wa.H -45.34 -15.340 -0.34000 15.660 46.66
## nodematch.race..wa.B -5.48 -2.480 -0.47985 1.520 6.52
## nodematch.race..wa.H -13.18 -5.181 -0.18150 4.819 13.82
## nodematch.race..wa.O -64.08 -22.081 -0.08078 21.919 63.92
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.75435602
## nodefactor.deg.main.1 0.75435602 1.00000000
## nodefactor.race..wa.B 0.33851232 0.22364886
## nodefactor.race..wa.H 0.46542335 0.39531235
## nodematch.race..wa.B 0.05682146 0.03412326
## nodematch.race..wa.H 0.12960788 0.11737225
## nodematch.race..wa.O 0.78589064 0.58049480
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.338512322 0.465423354
## nodefactor.deg.main.1 0.223648857 0.395312348
## nodefactor.race..wa.B 1.000000000 0.102019603
## nodefactor.race..wa.H 0.102019603 1.000000000
## nodematch.race..wa.B 0.318708266 0.001460072
## nodematch.race..wa.H -0.009040549 0.501713153
## nodematch.race..wa.O -0.026690472 -0.034467039
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.056821459 0.129607878
## nodefactor.deg.main.1 0.034123262 0.117372255
## nodefactor.race..wa.B 0.318708266 -0.009040549
## nodefactor.race..wa.H 0.001460072 0.501713153
## nodematch.race..wa.B 1.000000000 0.011906581
## nodematch.race..wa.H 0.011906581 1.000000000
## nodematch.race..wa.O -0.001822617 0.006844409
## nodematch.race..wa.O
## edges 0.785890636
## nodefactor.deg.main.1 0.580494801
## nodefactor.race..wa.B -0.026690472
## nodefactor.race..wa.H -0.034467039
## nodematch.race..wa.B -0.001822617
## nodematch.race..wa.H 0.006844409
## nodematch.race..wa.O 1.000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.009293063 0.008011998 -0.020722780
## Lag 2e+05 -0.018081756 -0.006216810 0.005627376
## Lag 3e+05 0.011383663 0.026950876 -0.010029064
## Lag 4e+05 -0.013430660 0.003339623 -0.003574175
## Lag 5e+05 0.003813957 0.010677571 0.013976224
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0080016775 -0.001476886 -0.019302207
## Lag 2e+05 -0.0065974590 0.014037323 -0.008173921
## Lag 3e+05 -0.0023484951 -0.044061131 -0.011715521
## Lag 4e+05 0.0001880001 0.018967260 0.006887641
## Lag 5e+05 -0.0234953632 0.014294712 -0.016379106
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.002098923
## Lag 2e+05 -0.017595895
## Lag 3e+05 0.027289908
## Lag 4e+05 -0.014830819
## Lag 5e+05 0.001936321
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.018237694 -0.040697309 0.005015403
## Lag 2e+05 0.020465137 -0.006178059 0.018563952
## Lag 3e+05 0.023261821 0.002307541 -0.009131595
## Lag 4e+05 -0.001057455 -0.007313872 -0.003350575
## Lag 5e+05 0.013408178 0.012545281 0.006513220
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.012684860 0.0010828510 -0.028259959
## Lag 2e+05 -0.003139790 -0.0133935329 0.016644511
## Lag 3e+05 0.022906648 -0.0001421568 0.013071676
## Lag 4e+05 0.008604890 -0.0317307004 -0.009207429
## Lag 5e+05 0.006506597 -0.0171964991 -0.005462904
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.025331549
## Lag 2e+05 -0.004345648
## Lag 3e+05 0.033991344
## Lag 4e+05 -0.002791178
## Lag 5e+05 0.012098907
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.001186623 0.005745008 0.001751251
## Lag 2e+05 0.018183195 0.014756930 0.039419008
## Lag 3e+05 -0.027444894 -0.012936803 -0.016645592
## Lag 4e+05 -0.014858704 -0.008436175 0.006630207
## Lag 5e+05 -0.006151160 0.033499247 -0.013271629
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.016620645 -0.010114631 -0.017944590
## Lag 2e+05 0.019775456 0.010546563 -0.008907330
## Lag 3e+05 -0.007675039 -0.041593110 0.010477988
## Lag 4e+05 0.013270999 -0.001443653 0.007536412
## Lag 5e+05 -0.004971104 0.025253299 0.002122503
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.008540368
## Lag 2e+05 0.006610328
## Lag 3e+05 0.004960897
## Lag 4e+05 -0.003118366
## Lag 5e+05 -0.001724229
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000e+00
## Lag 1e+05 0.022948940 0.003274508 -1.424741e-02
## Lag 2e+05 -0.006990688 -0.010982905 2.207110e-02
## Lag 3e+05 0.007820858 -0.008017556 6.099203e-03
## Lag 4e+05 0.027123351 -0.003068464 1.658893e-03
## Lag 5e+05 -0.016889843 -0.005636265 4.725629e-05
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.046468614 0.0186958556 -0.002738294
## Lag 2e+05 -0.004488874 -0.0002844727 0.001308469
## Lag 3e+05 -0.014537653 -0.0066132463 -0.016067678
## Lag 4e+05 -0.018374810 0.0166350271 -0.010933197
## Lag 5e+05 -0.012693726 0.0175186586 -0.003243118
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.001655538
## Lag 2e+05 -0.003940169
## Lag 3e+05 -0.007186782
## Lag 4e+05 0.032758649
## Lag 5e+05 0.002577675
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 -0.01993311 0.016288313 -0.008220466
## Lag 2e+05 -0.02196141 -0.001563550 0.003173376
## Lag 3e+05 0.02122909 0.002312400 0.006011395
## Lag 4e+05 -0.01337656 -0.016383689 -0.026885529
## Lag 5e+05 0.01363626 -0.001660243 -0.020797532
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.001401362 -0.041404272 0.012969389
## Lag 2e+05 0.009289617 -0.016352658 0.002479853
## Lag 3e+05 0.025378040 -0.008827061 0.006612844
## Lag 4e+05 -0.005259818 0.004097936 -0.013765971
## Lag 5e+05 -0.028221806 -0.021059962 -0.018229630
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.010578101
## Lag 2e+05 -0.011190862
## Lag 3e+05 -0.020801603
## Lag 4e+05 -0.007749814
## Lag 5e+05 0.009735176
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.003653517 -0.0015679033 -0.017660505
## Lag 2e+05 0.014558174 0.0005850216 0.009973016
## Lag 3e+05 0.017305341 0.0142406622 0.014533012
## Lag 4e+05 0.012583340 -0.0064996687 0.051125473
## Lag 5e+05 -0.003094327 -0.0121423641 -0.002365358
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.034175979 -0.005700256 -0.0047655775
## Lag 2e+05 0.023990043 0.021851469 0.0175416039
## Lag 3e+05 0.004251331 0.006276522 -0.0173563281
## Lag 4e+05 0.010362153 -0.013613391 0.0034778828
## Lag 5e+05 -0.009587600 -0.015199305 -0.0009915874
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.012566762
## Lag 2e+05 0.023156755
## Lag 3e+05 0.013737604
## Lag 4e+05 0.004362603
## Lag 5e+05 -0.031385819
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.019435737 0.011850949 0.003078150
## Lag 2e+05 0.005161988 0.007829524 0.002324990
## Lag 3e+05 0.037654383 0.027480585 -0.003468542
## Lag 4e+05 0.005361665 -0.004993771 -0.016217411
## Lag 5e+05 0.003055633 -0.001018698 -0.009813130
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.004052237 0.032301484 0.001126623
## Lag 2e+05 -0.015614977 -0.009034676 -0.013694880
## Lag 3e+05 0.005133637 -0.027066454 0.002654601
## Lag 4e+05 -0.016010975 -0.038736783 -0.006065059
## Lag 5e+05 -0.006401800 -0.001323357 0.015247604
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0345187188
## Lag 2e+05 0.0146041642
## Lag 3e+05 0.0295160548
## Lag 4e+05 -0.0001618107
## Lag 5e+05 -0.0024724736
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.013131415 0.017879715 -0.015151666
## Lag 2e+05 0.012990075 -0.015551721 0.037727969
## Lag 3e+05 0.007594693 -0.005010625 0.014582723
## Lag 4e+05 -0.023607472 -0.009327895 0.017416738
## Lag 5e+05 0.022588059 -0.005881848 -0.001589607
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.036062130 0.013677284 0.018135151
## Lag 2e+05 0.015572939 0.005823085 0.018700011
## Lag 3e+05 0.013693404 -0.008601553 0.003613198
## Lag 4e+05 0.001012469 -0.007943640 0.002009244
## Lag 5e+05 -0.009931664 0.002792836 -0.015222331
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.023766282
## Lag 2e+05 0.012977398
## Lag 3e+05 -0.011083680
## Lag 4e+05 0.006002424
## Lag 5e+05 0.035954519
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7384 0.4747 -0.6884
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 1.4355 -0.6245 0.8329
## nodematch.race..wa.O
## 0.1942
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.4602634 0.6350053 0.4912313
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.1511316 0.5323010 0.4048863
## nodematch.race..wa.O
## 0.8460135
## Joint P-value (lower = worse): 0.8461884 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.81764 1.00605 1.14825
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.40055 0.03797 -0.20965
## nodematch.race..wa.O
## 1.77587
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.06911935 0.31439271 0.25086723
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.68874942 0.96971459 0.83394117
## nodematch.race..wa.O
## 0.07575434
## Joint P-value (lower = worse): 0.3980385 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -2.3303 -1.6643 -0.5194
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -1.5694 0.2624 -0.3911
## nodematch.race..wa.O
## -1.4138
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.01979061 0.09605812 0.60346459
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.11656477 0.79300506 0.69575892
## nodematch.race..wa.O
## 0.15741393
## Joint P-value (lower = worse): 0.4895735 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5028 0.6917 -0.3741
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 1.1209 -1.5897 -0.0983
## nodematch.race..wa.O
## -0.1746
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6151032 0.4890947 0.7083326
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.2623309 0.1119015 0.9216978
## nodematch.race..wa.O
## 0.8613914
## Joint P-value (lower = worse): 0.7396147 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.15661 -0.09658 0.43925
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -0.18341 1.59057 -0.18995
## nodematch.race..wa.O
## 0.14598
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.8755495 0.9230637 0.6604794
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.8544742 0.1117072 0.8493477
## nodematch.race..wa.O
## 0.8839356
## Joint P-value (lower = worse): 0.6088548 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.69307 0.72823 0.94701
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -1.23936 0.04255 -0.44423
## nodematch.race..wa.O
## 1.50225
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.4882660 0.4664703 0.3436337
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.2152140 0.9660640 0.6568737
## nodematch.race..wa.O
## 0.1330321
## Joint P-value (lower = worse): 0.4732055 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3352 1.3624 1.6588
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 1.8390 0.7001 2.1448
## nodematch.race..wa.O
## -0.8624
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.73745236 0.17306928 0.09715780
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.06591712 0.48384679 0.03197160
## nodematch.race..wa.O
## 0.38848316
## Joint P-value (lower = worse): 0.1540432 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.11368 0.21635 0.03121
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## -1.28667 -1.76471 -0.23792
## nodematch.race..wa.O
## 0.16016
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.90949261 0.82871606 0.97510166
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## 0.19821006 0.07761215 0.81194331
## nodematch.race..wa.O
## 0.87275159
## Joint P-value (lower = worse): 0.2649218 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 5
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.15557 40.043 0.23119 0.23120
## nodefactor.deg.main.1 0.01103 45.033 0.26000 0.25999
## nodefactor.race..wa.B -0.07180 16.122 0.09308 0.09379
## nodefactor.race..wa.H 0.33197 23.643 0.13650 0.13716
## nodefactor.region.EW 0.66843 20.094 0.11601 0.11516
## nodefactor.region.OW -0.22487 38.261 0.22090 0.22090
## nodematch.race..wa.B -0.11268 2.853 0.01647 0.01638
## nodematch.race..wa.H -0.03306 6.960 0.04018 0.03948
## nodematch.race..wa.O -0.29365 32.780 0.18925 0.18787
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -77.50 -27.500 0.50000 27.500 78.50
## nodefactor.deg.main.1 -88.00 -30.000 0.00000 30.000 89.00
## nodefactor.race..wa.B -31.52 -10.517 -0.51680 10.483 31.48
## nodefactor.race..wa.H -46.34 -15.340 0.66000 15.660 47.66
## nodefactor.region.EW -38.48 -13.482 0.51800 14.518 40.52
## nodefactor.region.OW -74.59 -25.585 -0.58550 25.415 75.41
## nodematch.race..wa.B -5.48 -2.480 -0.47985 1.520 5.52
## nodematch.race..wa.H -13.18 -5.181 -0.18150 4.819 13.82
## nodematch.race..wa.O -64.08 -23.081 -0.08078 21.919 64.92
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.75345727
## nodefactor.deg.main.1 0.75345727 1.00000000
## nodefactor.race..wa.B 0.33845329 0.21689108
## nodefactor.race..wa.H 0.46819134 0.40310865
## nodefactor.region.EW 0.41379077 0.32375711
## nodefactor.region.OW 0.68390840 0.52010474
## nodematch.race..wa.B 0.05520395 0.03302513
## nodematch.race..wa.H 0.12805733 0.13228423
## nodematch.race..wa.O 0.78250545 0.57760841
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.33845329 0.468191338
## nodefactor.deg.main.1 0.21689108 0.403108649
## nodefactor.race..wa.B 1.00000000 0.111030569
## nodefactor.race..wa.H 0.11103057 1.000000000
## nodefactor.region.EW 0.08695070 0.298396112
## nodefactor.region.OW 0.21378314 0.302690399
## nodematch.race..wa.B 0.31689084 -0.007312129
## nodematch.race..wa.H -0.01121136 0.501694774
## nodematch.race..wa.O -0.03362265 -0.036938569
## nodefactor.region.EW nodefactor.region.OW
## edges 0.413790766 0.68390840
## nodefactor.deg.main.1 0.323757113 0.52010474
## nodefactor.race..wa.B 0.086950699 0.21378314
## nodefactor.race..wa.H 0.298396112 0.30269040
## nodefactor.region.EW 1.000000000 0.13004633
## nodefactor.region.OW 0.130046326 1.00000000
## nodematch.race..wa.B -0.004221274 0.03056970
## nodematch.race..wa.H 0.110545603 0.07830264
## nodematch.race..wa.O 0.287710994 0.55195747
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.0552039514 0.128057328
## nodefactor.deg.main.1 0.0330251299 0.132284233
## nodefactor.race..wa.B 0.3168908392 -0.011211360
## nodefactor.race..wa.H -0.0073121289 0.501694774
## nodefactor.region.EW -0.0042212743 0.110545603
## nodefactor.region.OW 0.0305697022 0.078302636
## nodematch.race..wa.B 1.0000000000 -0.002071752
## nodematch.race..wa.H -0.0020717517 1.000000000
## nodematch.race..wa.O -0.0009486707 0.004490205
## nodematch.race..wa.O
## edges 0.7825054455
## nodefactor.deg.main.1 0.5776084054
## nodefactor.race..wa.B -0.0336226496
## nodefactor.race..wa.H -0.0369385690
## nodefactor.region.EW 0.2877109936
## nodefactor.region.OW 0.5519574695
## nodematch.race..wa.B -0.0009486707
## nodematch.race..wa.H 0.0044902047
## nodematch.race..wa.O 1.0000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.0155776719 -0.0168773039 0.005430397
## Lag 2e+05 0.0027718609 -0.0001376195 -0.029364591
## Lag 3e+05 0.0004772579 0.0089367378 0.015467548
## Lag 4e+05 -0.0008029413 -0.0058546594 -0.014468658
## Lag 5e+05 -0.0079982515 -0.0172002366 0.021607746
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.006537826 -0.008071934 0.001327376
## Lag 2e+05 0.026296447 0.014683188 0.005850599
## Lag 3e+05 0.004118184 0.019160936 0.006175374
## Lag 4e+05 0.009716777 0.018350626 -0.018257224
## Lag 5e+05 0.017242548 0.003867631 -0.024216917
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.017887494 0.0195345824 -0.010411918
## Lag 2e+05 0.008976638 0.0059502466 0.005590610
## Lag 3e+05 0.006751088 -0.0004818955 0.004119323
## Lag 4e+05 0.022051434 0.0053382565 -0.005117515
## Lag 5e+05 -0.032673970 0.0046287553 0.003539979
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0009359386 0.010891170 -0.010269036
## Lag 2e+05 0.0079056044 0.007410431 -0.015774792
## Lag 3e+05 -0.0326617413 0.006680648 0.019503027
## Lag 4e+05 -0.0018055029 0.005509050 -0.007932271
## Lag 5e+05 0.0012825739 -0.013797275 -0.012165708
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.0000000000 1.000000000
## Lag 1e+05 0.01021976 0.0180414165 0.007544616
## Lag 2e+05 -0.01050902 0.0201989046 -0.020130129
## Lag 3e+05 -0.01810262 -0.0051635342 -0.023956593
## Lag 4e+05 0.01482104 -0.0080653834 -0.018622113
## Lag 5e+05 -0.04386961 0.0005156574 0.001199063
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 -0.023006553 0.015284417 0.01541778
## Lag 2e+05 0.005514688 -0.021099801 0.01590598
## Lag 3e+05 0.015080449 0.008437308 -0.03454597
## Lag 4e+05 -0.014024572 -0.004507218 -0.01783006
## Lag 5e+05 0.004573674 -0.020195620 0.01366259
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.0000000000 1.0000000000
## Lag 1e+05 -0.0072106592 -0.0002629941 -0.0021847694
## Lag 2e+05 -0.0007276835 0.0210744912 0.0326632708
## Lag 3e+05 -0.0303260322 -0.0107311152 0.0007500169
## Lag 4e+05 -0.0070347787 -0.0061653078 -0.0059201589
## Lag 5e+05 -0.0120867226 -0.0118707273 0.0141423435
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.003248183 -0.0172346199 -0.011413453
## Lag 2e+05 0.018272537 -0.0509328592 -0.002297919
## Lag 3e+05 -0.013197218 0.0001011912 -0.016819210
## Lag 4e+05 0.006739119 -0.0044089465 0.002312680
## Lag 5e+05 -0.013426141 0.0020308061 -0.006129954
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 -0.003279911 -0.01698751 -0.007029950
## Lag 2e+05 -0.030959165 0.01279362 -0.001144673
## Lag 3e+05 -0.001861938 -0.02260940 -0.031265420
## Lag 4e+05 0.016462522 -0.01311573 0.002561516
## Lag 5e+05 0.011212173 -0.01500510 -0.006671556
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.013419079 -0.003968737 0.007325640
## Lag 2e+05 0.005777806 0.008600556 0.012425807
## Lag 3e+05 -0.002403804 -0.011952942 0.001401465
## Lag 4e+05 -0.011825942 0.007599393 0.005656423
## Lag 5e+05 0.025765076 0.014499201 0.010274951
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0045992927 -0.007215112 -0.013662434
## Lag 2e+05 -0.0170181626 0.001602423 -0.009471421
## Lag 3e+05 -0.0121272991 -0.018578935 0.026582384
## Lag 4e+05 0.0007868562 -0.022364469 -0.005325528
## Lag 5e+05 0.0194036923 -0.003293690 0.014394572
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.001753176 0.014883522 -0.005222238
## Lag 2e+05 0.026773877 -0.027377844 0.016009146
## Lag 3e+05 -0.003216809 -0.003241682 0.016157091
## Lag 4e+05 -0.006112816 -0.008519223 0.004229298
## Lag 5e+05 -0.009555178 -0.006692635 0.013894126
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.010542713 0.010177741 0.0001473984
## Lag 2e+05 -0.024039347 -0.013291550 -0.0115452943
## Lag 3e+05 0.008855302 0.002487855 0.0206501588
## Lag 4e+05 -0.004425617 -0.007962527 -0.0250465686
## Lag 5e+05 -0.004690670 0.019010122 -0.0075950975
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.002929805 0.001093819 -0.022160694
## Lag 2e+05 -0.018377018 -0.002393030 -0.003016368
## Lag 3e+05 0.007923728 0.001774896 0.006480680
## Lag 4e+05 -0.039232096 -0.006586827 -0.009124284
## Lag 5e+05 -0.004028321 -0.022057672 0.011136258
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.010688127 0.014437763 -0.005518893
## Lag 2e+05 -0.009000773 0.014114123 -0.027949934
## Lag 3e+05 -0.016616711 -0.006886396 -0.018890577
## Lag 4e+05 0.001552123 -0.014073027 -0.013864917
## Lag 5e+05 0.017188564 -0.031278642 0.017284321
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.010654116 0.009152485 -0.01166566
## Lag 2e+05 0.006836376 0.017371837 0.01701649
## Lag 3e+05 -0.001310423 0.003289212 0.00730490
## Lag 4e+05 -0.012774622 -0.017055873 -0.01806714
## Lag 5e+05 0.008143080 -0.004749170 -0.01525660
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.034988414 -0.009637860 0.003001375
## Lag 2e+05 0.007064118 0.022320561 -0.024108999
## Lag 3e+05 -0.015083396 -0.030332067 -0.016385491
## Lag 4e+05 0.006745265 0.003943670 -0.004398145
## Lag 5e+05 0.010464293 -0.001394617 -0.013764165
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.001756447 0.0002853853 0.003555721
## Lag 2e+05 -0.002491891 0.0154954242 -0.009143844
## Lag 3e+05 0.026049657 -0.0133481192 0.001831496
## Lag 4e+05 -0.013704124 0.0495370550 -0.015568125
## Lag 5e+05 0.006570685 -0.0016052403 0.028217604
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.022115509 -0.001161409 -0.010607769
## Lag 2e+05 0.006562722 0.002153508 -0.005158655
## Lag 3e+05 -0.007478078 -0.037022942 0.002618603
## Lag 4e+05 0.025670402 0.020364905 0.015157453
## Lag 5e+05 -0.013981280 -0.007319128 0.005140514
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.0070675106 -0.0032213699 0.014046307
## Lag 2e+05 0.0497291058 -0.0106181781 0.007957787
## Lag 3e+05 -0.0055366954 0.0007378411 -0.017621268
## Lag 4e+05 -0.0004487348 0.0074009924 0.020561450
## Lag 5e+05 0.0161310212 0.0303864883 -0.034492764
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.008889246 -0.005219654 0.007864813
## Lag 2e+05 -0.015543786 0.020385866 0.008364800
## Lag 3e+05 0.015795089 0.011548535 0.020701867
## Lag 4e+05 0.014920328 -0.023197911 0.031904457
## Lag 5e+05 -0.005205944 0.026065938 -0.004210633
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.001152457 0.009556988 0.027902683
## Lag 2e+05 -0.027515558 -0.016407706 -0.014807059
## Lag 3e+05 -0.028285236 -0.004044212 -0.014392301
## Lag 4e+05 -0.019349655 -0.001528833 -0.004576124
## Lag 5e+05 0.011268722 -0.007050361 -0.007254703
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 -0.00201844 -0.023094334 0.020385408
## Lag 2e+05 -0.01693260 -0.017819638 0.004418618
## Lag 3e+05 0.01486055 0.005929922 -0.010205451
## Lag 4e+05 -0.01138724 0.014734141 -0.002664948
## Lag 5e+05 0.00800532 -0.021725905 -0.012664973
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.002030059 0.022419321 0.008531023
## Lag 2e+05 0.020710362 -0.025762224 -0.028850157
## Lag 3e+05 -0.024766716 0.003318331 -0.023780602
## Lag 4e+05 -0.022539739 -0.016592572 0.006065647
## Lag 5e+05 0.018173312 0.008027753 -0.020830440
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.2922 -0.3009 -1.8090
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.1453 -0.5074 0.3174
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.2690 -0.7501 1.3616
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.77013188 0.76348342 0.07045709
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.25209554 0.61188806 0.75092587
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.20443357 0.45318258 0.17333142
## Joint P-value (lower = worse): 0.5328707 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.6033 -0.4920 -0.7048
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.5829 0.2892 -1.2304
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.7658 0.2324 -0.1326
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5462842 0.6226907 0.4809219
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5599855 0.7724257 0.2185452
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.4437810 0.8162342 0.8945129
## Joint P-value (lower = worse): 0.9199044 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.85935 0.02565 -0.65182
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.15385 -1.41148 -0.13948
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.55235 -0.31387 -1.20225
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3901500 0.9795355 0.5145173
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8777245 0.1581028 0.8890712
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.5807070 0.7536166 0.2292666
## Joint P-value (lower = worse): 0.8138731 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.63820 0.05525 0.14317
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.13206 0.89619 0.19908
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.71678 1.29414 0.80979
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5233416 0.9559410 0.8861596
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8949358 0.3701497 0.8422030
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.4735115 0.1956158 0.4180612
## Joint P-value (lower = worse): 0.559907 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.53543 -0.45590 -1.20356
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.01205 0.55669 0.69577
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.49903 -1.01636 1.08642
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.5923512 0.6484638 0.2287589
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.9903885 0.5777396 0.4865742
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.6177612 0.3094575 0.2772949
## Joint P-value (lower = worse): 0.6567077 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.5923 -1.5416 -0.8391
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.5566 -0.8338 -1.4532
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.9545 1.1016 -0.6500
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.1113158 0.1231625 0.4014238
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.1195548 0.4043869 0.1461760
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.3398440 0.2706254 0.5157003
## Joint P-value (lower = worse): 0.3917235 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.8710 0.1817 0.3839
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 1.0736 1.0140 1.4675
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.3844 -0.5347 -0.1926
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3837566 0.8558347 0.7010630
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.2830169 0.3105639 0.1422514
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.7006767 0.5928299 0.8472647
## Joint P-value (lower = worse): 0.476547 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.03613 -0.21532 0.84866
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.34027 -0.54939 0.44299
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.32442 0.32812 0.06498
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.9711771 0.8295182 0.3960717
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.7336555 0.5827377 0.6577709
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1853652 0.7428233 0.9481861
## Joint P-value (lower = worse): 0.9453042 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 6
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -0.85347 40.043 0.23119 0.22811
## nodefactor.deg.main.1 -1.22360 45.293 0.26150 0.25749
## nodefactor.race..wa.B -0.12907 16.001 0.09238 0.09189
## nodefactor.race..wa.H -0.61343 23.500 0.13568 0.13613
## nodefactor.region.EW -0.28337 20.058 0.11580 0.11358
## nodefactor.region.OW -0.50533 38.256 0.22087 0.21823
## nodematch.race..wa.B 0.01832 2.897 0.01672 0.01666
## nodematch.race..wa.H -0.09230 6.938 0.04006 0.03976
## nodematch.race..wa.O -0.19218 32.681 0.18869 0.18639
## absdiff.sqrt.age -0.60442 45.117 0.26048 0.25827
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -79.50 -27.500 -0.50000 26.500 78.50
## nodefactor.deg.main.1 -89.00 -32.000 -1.00000 29.000 89.00
## nodefactor.race..wa.B -31.52 -11.517 -0.51680 10.483 31.48
## nodefactor.race..wa.H -47.34 -16.340 -0.34000 15.660 45.66
## nodefactor.region.EW -38.48 -13.482 -0.48200 13.518 39.52
## nodefactor.region.OW -74.59 -26.585 -0.58550 25.415 74.41
## nodematch.race..wa.B -5.48 -2.480 -0.47985 1.520 6.52
## nodematch.race..wa.H -13.18 -5.181 -0.18150 4.819 13.82
## nodematch.race..wa.O -64.08 -22.081 -0.08078 21.919 64.92
## absdiff.sqrt.age -87.86 -31.258 -1.14773 29.720 88.74
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.75480668
## nodefactor.deg.main.1 0.75480668 1.00000000
## nodefactor.race..wa.B 0.34217222 0.23349580
## nodefactor.race..wa.H 0.46695909 0.40259275
## nodefactor.region.EW 0.42468980 0.33448998
## nodefactor.region.OW 0.67894673 0.51226908
## nodematch.race..wa.B 0.06261356 0.03920251
## nodematch.race..wa.H 0.12390244 0.11529131
## nodematch.race..wa.O 0.78629289 0.57512114
## absdiff.sqrt.age 0.73656977 0.55645693
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.34217222 0.4669590865
## nodefactor.deg.main.1 0.23349580 0.4025927532
## nodefactor.race..wa.B 1.00000000 0.1081000103
## nodefactor.race..wa.H 0.10810001 1.0000000000
## nodefactor.region.EW 0.09725968 0.3013521972
## nodefactor.region.OW 0.21012703 0.2904828655
## nodematch.race..wa.B 0.31320138 0.0000761264
## nodematch.race..wa.H -0.01485683 0.4976074049
## nodematch.race..wa.O -0.02361540 -0.0322653364
## absdiff.sqrt.age 0.25954111 0.3398164295
## nodefactor.region.EW nodefactor.region.OW
## edges 0.42468980 0.67894673
## nodefactor.deg.main.1 0.33448998 0.51226908
## nodefactor.race..wa.B 0.09725968 0.21012703
## nodefactor.race..wa.H 0.30135220 0.29048287
## nodefactor.region.EW 1.00000000 0.12690300
## nodefactor.region.OW 0.12690300 1.00000000
## nodematch.race..wa.B 0.01001047 0.03158038
## nodematch.race..wa.H 0.11680094 0.06469305
## nodematch.race..wa.O 0.29512292 0.55633739
## absdiff.sqrt.age 0.31493189 0.49819399
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.0626135648 0.123902443
## nodefactor.deg.main.1 0.0392025120 0.115291307
## nodefactor.race..wa.B 0.3132013814 -0.014856828
## nodefactor.race..wa.H 0.0000761264 0.497607405
## nodefactor.region.EW 0.0100104720 0.116800945
## nodefactor.region.OW 0.0315803799 0.064693046
## nodematch.race..wa.B 1.0000000000 0.005454956
## nodematch.race..wa.H 0.0054549557 1.000000000
## nodematch.race..wa.O 0.0101588545 0.007022237
## absdiff.sqrt.age 0.0466051182 0.085842705
## nodematch.race..wa.O absdiff.sqrt.age
## edges 0.786292886 0.73656977
## nodefactor.deg.main.1 0.575121137 0.55645693
## nodefactor.race..wa.B -0.023615400 0.25954111
## nodefactor.race..wa.H -0.032265336 0.33981643
## nodefactor.region.EW 0.295122921 0.31493189
## nodefactor.region.OW 0.556337391 0.49819399
## nodematch.race..wa.B 0.010158854 0.04660512
## nodematch.race..wa.H 0.007022237 0.08584271
## nodematch.race..wa.O 1.000000000 0.57958994
## absdiff.sqrt.age 0.579589937 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.001106597 0.004582242 -0.0031593475
## Lag 2e+05 -0.020765123 -0.022332441 0.0199925795
## Lag 3e+05 0.020794922 0.020004835 -0.0002757238
## Lag 4e+05 -0.007201211 -0.018799993 -0.0074313649
## Lag 5e+05 0.004522960 -0.043506911 0.0028203454
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.006162301 0.017809876 0.000298485
## Lag 2e+05 -0.011995140 -0.002756002 -0.025201623
## Lag 3e+05 -0.003521245 0.021521112 -0.001060858
## Lag 4e+05 0.004229335 -0.009326315 -0.010209185
## Lag 5e+05 -0.011623757 -0.001300962 -0.005526272
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.002163765 0.006831619 -0.0212108962
## Lag 2e+05 -0.028153463 -0.030185062 -0.0175654164
## Lag 3e+05 0.007521811 0.002784691 -0.0001801002
## Lag 4e+05 -0.004454475 -0.001585238 -0.0108710916
## Lag 5e+05 0.009994769 -0.004331194 0.0110472169
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.001954283
## Lag 2e+05 -0.022332160
## Lag 3e+05 0.005050469
## Lag 4e+05 -0.012282806
## Lag 5e+05 0.013869389
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0157592247 -0.014943574 0.004496101
## Lag 2e+05 -0.0078511055 0.001082632 0.020665876
## Lag 3e+05 0.0034878569 0.003989342 -0.026456928
## Lag 4e+05 -0.0003947242 0.013735763 0.012553970
## Lag 5e+05 0.0046880175 -0.006850831 0.009521430
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.002811276 0.010094332 -0.034619058
## Lag 2e+05 -0.035029463 0.005078504 0.005369494
## Lag 3e+05 0.014030772 0.012939761 -0.046214979
## Lag 4e+05 0.008443736 -0.000629914 0.006578117
## Lag 5e+05 -0.035224011 -0.003356241 0.021876824
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.022449455 0.007968422 -0.002272499
## Lag 2e+05 -0.010427772 -0.021695154 -0.016686202
## Lag 3e+05 -0.020060486 0.009659008 -0.003412187
## Lag 4e+05 0.008044869 -0.011038593 -0.005492871
## Lag 5e+05 -0.009689088 0.005905722 0.010610707
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.002793681
## Lag 2e+05 -0.005485532
## Lag 3e+05 0.006237268
## Lag 4e+05 -0.031217205
## Lag 5e+05 -0.015371519
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.0000000000 1.00000000
## Lag 1e+05 -0.0261038716 -0.0143784064 0.01647223
## Lag 2e+05 0.0299717772 0.0224651079 0.02910889
## Lag 3e+05 0.0009265022 0.0007632403 0.01663183
## Lag 4e+05 0.0065082343 -0.0113303017 -0.02167512
## Lag 5e+05 0.0225628206 0.0111823803 0.02121583
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.027927516 -0.010515198 -0.026625409
## Lag 2e+05 0.011746960 -0.004136206 0.011301111
## Lag 3e+05 0.002403153 0.006664457 -0.009931964
## Lag 4e+05 0.017458836 0.016436606 0.006236205
## Lag 5e+05 -0.015239173 0.017560236 0.001689468
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 -0.033240197 -0.003151408 -0.01888296
## Lag 2e+05 -0.007695941 -0.003126962 0.02079403
## Lag 3e+05 -0.022749205 -0.001460359 0.01243862
## Lag 4e+05 0.012525616 0.013404273 0.02254698
## Lag 5e+05 -0.027669913 -0.004779607 0.02639782
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.030707018
## Lag 2e+05 0.006860491
## Lag 3e+05 -0.005087096
## Lag 4e+05 -0.007608633
## Lag 5e+05 0.013721362
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.0009702663 0.0211391304 -0.017441653
## Lag 2e+05 -0.0087857946 -0.0006329994 -0.029560734
## Lag 3e+05 -0.0053802787 -0.0075740411 0.023316224
## Lag 4e+05 0.0069513825 0.0020230389 -0.007712845
## Lag 5e+05 -0.0115225249 0.0158596986 0.010603881
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 -0.008876534 0.02156196 0.003353123
## Lag 2e+05 -0.025098314 -0.01857287 -0.020141823
## Lag 3e+05 0.011013267 0.02863067 -0.006882306
## Lag 4e+05 -0.002887517 -0.03646250 -0.003795336
## Lag 5e+05 -0.024027018 0.02223268 -0.010676339
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.018087556 -0.036776393 0.0120571166
## Lag 2e+05 0.013791566 0.002308595 0.0050206717
## Lag 3e+05 0.002324272 0.002423513 -0.0067656610
## Lag 4e+05 0.031314132 -0.006402121 0.0006060183
## Lag 5e+05 0.002593423 -0.015622417 0.0017303156
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.021508286
## Lag 2e+05 -0.010984526
## Lag 3e+05 0.005240194
## Lag 4e+05 0.009467763
## Lag 5e+05 -0.008764874
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.010179682 -0.020151141 -0.048314481
## Lag 2e+05 0.023460215 0.009684010 -0.003094904
## Lag 3e+05 -0.035384521 -0.013877904 -0.014216655
## Lag 4e+05 -0.002910053 0.003425068 -0.010953245
## Lag 5e+05 -0.009635453 0.012309316 0.009597170
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0132127041 0.002654046 -0.015511432
## Lag 2e+05 -0.0057738370 0.003599510 0.008150541
## Lag 3e+05 0.0155891080 -0.030331747 -0.019719850
## Lag 4e+05 -0.0193867571 0.004390452 0.002856458
## Lag 5e+05 0.0005314615 -0.027222517 -0.001882021
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 -0.01199087 0.01696358 -0.001644111
## Lag 2e+05 -0.01021342 0.01059156 0.009359877
## Lag 3e+05 0.01416565 0.01430420 -0.006409555
## Lag 4e+05 -0.02330695 -0.02346616 -0.016616755
## Lag 5e+05 -0.01270349 0.00306568 -0.016019942
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.026491880
## Lag 2e+05 -0.002657270
## Lag 3e+05 -0.010595987
## Lag 4e+05 -0.005134211
## Lag 5e+05 -0.006018917
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.002999248 -0.013159909 0.012944411
## Lag 2e+05 -0.002165625 -0.006095738 0.008666305
## Lag 3e+05 -0.016161423 0.004827580 0.009726764
## Lag 4e+05 0.002779210 0.016077519 -0.005547820
## Lag 5e+05 -0.017331309 -0.007370630 -0.015391818
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.008893769 0.009024274 0.0034245407
## Lag 2e+05 0.012506623 0.016839003 -0.0024306402
## Lag 3e+05 -0.009381559 0.003784774 -0.0095820279
## Lag 4e+05 -0.001977041 -0.016428994 -0.0002933984
## Lag 5e+05 -0.008964214 -0.018354700 -0.0035974481
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.008079179 -0.024600190 0.01502831
## Lag 2e+05 -0.016884256 0.007951379 -0.00541248
## Lag 3e+05 0.015007341 -0.013555652 -0.02967566
## Lag 4e+05 0.019771821 0.020529682 -0.03445465
## Lag 5e+05 0.001967153 0.031308169 -0.01063953
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.006272615
## Lag 2e+05 0.006567439
## Lag 3e+05 -0.025781104
## Lag 4e+05 0.012900642
## Lag 5e+05 -0.013647239
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.017365731 -0.023294594 -0.008089333
## Lag 2e+05 -0.004625232 -0.006158168 0.015073705
## Lag 3e+05 -0.011097677 0.000136385 0.023258328
## Lag 4e+05 0.028041096 0.011212458 0.000191891
## Lag 5e+05 -0.039124006 -0.026723194 -0.018542918
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.014666548 -0.009883410 0.0040017613
## Lag 2e+05 -0.006878310 -0.005841600 -0.0060386083
## Lag 3e+05 -0.019277717 -0.002313732 0.0003674597
## Lag 4e+05 -0.014201804 -0.016039629 -0.0051094740
## Lag 5e+05 -0.009632885 -0.014515334 -0.0156964469
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.007720369 0.007798922 0.003456800
## Lag 2e+05 0.007868813 0.017402283 0.003281598
## Lag 3e+05 0.015888529 -0.018656641 0.001209753
## Lag 4e+05 -0.012182057 -0.022102180 0.023892960
## Lag 5e+05 0.021287296 0.021764637 -0.018821109
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.001285783
## Lag 2e+05 0.004028142
## Lag 3e+05 -0.004642598
## Lag 4e+05 0.010085015
## Lag 5e+05 -0.025398637
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.0093937422 -0.011441263 -0.0059252978
## Lag 2e+05 -0.0415312765 -0.032419924 0.0005346151
## Lag 3e+05 -0.0128304975 0.002389485 0.0004486710
## Lag 4e+05 -0.0006671555 -0.027551003 -0.0094440626
## Lag 5e+05 0.0121891473 -0.005770928 -0.0026574826
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.027026064 0.0056360533 0.017495673
## Lag 2e+05 -0.004862280 -0.0195448025 -0.003016671
## Lag 3e+05 -0.024745153 0.0059402277 -0.006669335
## Lag 4e+05 -0.003426496 -0.0163740566 -0.010733134
## Lag 5e+05 -0.006437120 -0.0006692625 -0.003832869
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.009110847 0.002181974 -0.0173067990
## Lag 2e+05 0.022163251 0.021232498 -0.0323793606
## Lag 3e+05 -0.006198292 -0.002375961 -0.0001700499
## Lag 4e+05 0.007422827 0.006826874 0.0199697038
## Lag 5e+05 -0.006100744 -0.006471887 0.0096501335
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.023255432
## Lag 2e+05 -0.035608355
## Lag 3e+05 -0.013568632
## Lag 4e+05 0.002674166
## Lag 5e+05 -0.023235379
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.96657 0.06281 0.56812
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.91022 0.95788 1.33481
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.15383 -0.78921 0.12913
## absdiff.sqrt.age
## 0.33798
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3337602 0.9499160 0.5699558
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.3627072 0.3381222 0.1819396
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8777418 0.4299916 0.8972527
## absdiff.sqrt.age
## 0.7353790
## Joint P-value (lower = worse): 0.7830986 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.21127 -0.09902 -0.09480
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.48987 -0.83234 -1.27376
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.33319 1.22473 0.11023
## absdiff.sqrt.age
## -1.41252
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.8326733 0.9211195 0.9244757
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.6242267 0.4052192 0.2027487
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1824691 0.2206779 0.9122254
## absdiff.sqrt.age
## 0.1577972
## Joint P-value (lower = worse): 0.1798488 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.45260 -0.01521 -0.49216
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.65535 -0.39828 0.52925
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.03038 -0.66350 1.06134
## absdiff.sqrt.age
## 0.27108
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6508381 0.9878640 0.6226046
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5122443 0.6904204 0.5966318
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.9757601 0.5070129 0.2885352
## absdiff.sqrt.age
## 0.7863277
## Joint P-value (lower = worse): 0.9910889 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.9498 2.2738 -0.6467
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.7675 0.9211 2.0748
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.8804 -0.4424 1.7682
## absdiff.sqrt.age
## 2.0084
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.05120063 0.02298075 0.51781522
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.44277820 0.35702352 0.03800596
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.06004931 0.65820193 0.07702264
## absdiff.sqrt.age
## 0.04460220
## Joint P-value (lower = worse): 0.1754572 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.1665 0.1385 1.2100
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8623 0.6305 -0.4384
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.6420 -0.2791 -0.6406
## absdiff.sqrt.age
## 0.2308
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.8677495 0.8898609 0.2262711
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.3885421 0.5283662 0.6610985
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1005803 0.7801660 0.5217870
## absdiff.sqrt.age
## 0.8174422
## Joint P-value (lower = worse): 0.8234019 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.06268 0.39080 0.02638
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.21723 0.08918 0.20058
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.03856 -0.72958 2.01093
## absdiff.sqrt.age
## 2.18769
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.28792513 0.69594780 0.97895395
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.22351619 0.92893580 0.84102969
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.96924231 0.46564858 0.04433237
## absdiff.sqrt.age
## 0.02869229
## Joint P-value (lower = worse): 0.3798001 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.05214 0.66707 -0.76925
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.76263 -0.02265 1.13641
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.88462 0.10588 0.87540
## absdiff.sqrt.age
## 0.64644
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.95841885 0.50472982 0.44174315
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.44568204 0.98192901 0.25578323
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.05948127 0.91568148 0.38135872
## absdiff.sqrt.age
## 0.51799648
## Joint P-value (lower = worse): 0.2800155 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.02933 -0.82762 0.52258
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.49262 -1.30776 -0.43156
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.91192 0.70794 -0.50823
## absdiff.sqrt.age
## -0.22579
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.9766013 0.4078851 0.6012657
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.6222824 0.1909550 0.6660648
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.3618109 0.4789805 0.6112900
## absdiff.sqrt.age
## 0.8213645
## Joint P-value (lower = worse): 0.6469447 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 7
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -0.22973 58.328 0.33676 0.35631
## nodefactor.deg.main.1 1.22413 61.089 0.35270 0.36859
## nodefactor.race..wa.B -0.22310 19.622 0.11329 0.11810
## nodefactor.race..wa.H -0.07100 29.713 0.17155 0.18661
## nodefactor.region.EW -0.21587 24.799 0.14318 0.14870
## nodefactor.region.OW -1.16113 50.129 0.28942 0.30834
## concurrent 0.14143 52.201 0.30138 0.31728
## nodematch.race..wa.B 0.05588 2.945 0.01700 0.01769
## nodematch.race..wa.H -0.13033 7.350 0.04243 0.04835
## nodematch.race..wa.O -0.18755 44.474 0.25677 0.26775
## absdiff.sqrt.age -0.69388 57.477 0.33184 0.33971
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -114.50 -39.500 -0.50000 39.500 113.50
## nodefactor.deg.main.1 -118.02 -40.000 1.00000 43.000 121.00
## nodefactor.race..wa.B -37.52 -13.517 -0.51680 12.483 38.48
## nodefactor.race..wa.H -58.34 -20.340 -0.34000 19.660 58.66
## nodefactor.region.EW -48.48 -17.482 -0.48200 16.518 48.52
## nodefactor.region.OW -97.59 -35.585 -1.58550 32.415 97.41
## concurrent -101.00 -35.000 0.00000 35.000 103.00
## nodematch.race..wa.B -5.48 -2.480 -0.47985 1.520 6.52
## nodematch.race..wa.H -14.18 -5.181 -0.18150 4.819 14.82
## nodematch.race..wa.O -87.08 -30.081 -0.08078 29.919 87.92
## absdiff.sqrt.age -112.09 -39.673 -1.09334 38.218 111.34
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.81648099
## nodefactor.deg.main.1 0.81648099 1.00000000
## nodefactor.race..wa.B 0.40069490 0.29865084
## nodefactor.race..wa.H 0.53958060 0.48444144
## nodefactor.region.EW 0.48690965 0.41004076
## nodefactor.region.OW 0.74926650 0.61203650
## concurrent 0.95213389 0.77489433
## nodematch.race..wa.B 0.07459228 0.04669405
## nodematch.race..wa.H 0.16999105 0.17040702
## nodematch.race..wa.O 0.84241778 0.67524164
## absdiff.sqrt.age 0.84363266 0.69280023
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.40069490 0.53958060
## nodefactor.deg.main.1 0.29865084 0.48444144
## nodefactor.race..wa.B 1.00000000 0.18170016
## nodefactor.race..wa.H 0.18170016 1.00000000
## nodefactor.region.EW 0.16328741 0.36088442
## nodefactor.region.OW 0.27549292 0.38270880
## concurrent 0.38516732 0.52912571
## nodematch.race..wa.B 0.36249731 0.01471362
## nodematch.race..wa.H 0.02252394 0.56014123
## nodematch.race..wa.O 0.07799820 0.11156935
## absdiff.sqrt.age 0.34222865 0.45110826
## nodefactor.region.EW nodefactor.region.OW concurrent
## edges 0.48690965 0.7492665 0.9521339
## nodefactor.deg.main.1 0.41004076 0.6120365 0.7748943
## nodefactor.race..wa.B 0.16328741 0.2754929 0.3851673
## nodefactor.race..wa.H 0.36088442 0.3827088 0.5291257
## nodefactor.region.EW 1.00000000 0.2286595 0.4663089
## nodefactor.region.OW 0.22865953 1.0000000 0.7118113
## concurrent 0.46630893 0.7118113 1.0000000
## nodematch.race..wa.B 0.01253585 0.0454551 0.0738186
## nodematch.race..wa.H 0.13864257 0.1182021 0.1720526
## nodematch.race..wa.O 0.36955361 0.6511509 0.7933305
## absdiff.sqrt.age 0.41026135 0.6298159 0.8019299
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.0745922819 0.169991053
## nodefactor.deg.main.1 0.0466940488 0.170407016
## nodefactor.race..wa.B 0.3624973053 0.022523943
## nodefactor.race..wa.H 0.0147136199 0.560141233
## nodefactor.region.EW 0.0125358518 0.138642565
## nodefactor.region.OW 0.0454550972 0.118202122
## concurrent 0.0738186008 0.172052639
## nodematch.race..wa.B 1.0000000000 0.008519252
## nodematch.race..wa.H 0.0085192515 1.000000000
## nodematch.race..wa.O 0.0006235499 0.010432993
## absdiff.sqrt.age 0.0660131786 0.140189301
## nodematch.race..wa.O absdiff.sqrt.age
## edges 0.8424177822 0.84363266
## nodefactor.deg.main.1 0.6752416411 0.69280023
## nodefactor.race..wa.B 0.0779981970 0.34222865
## nodefactor.race..wa.H 0.1115693528 0.45110826
## nodefactor.region.EW 0.3695536125 0.41026135
## nodefactor.region.OW 0.6511509003 0.62981593
## concurrent 0.7933304737 0.80192987
## nodematch.race..wa.B 0.0006235499 0.06601318
## nodematch.race..wa.H 0.0104329931 0.14018930
## nodematch.race..wa.O 1.0000000000 0.71124661
## absdiff.sqrt.age 0.7112466125 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.026594731 0.032844932 0.067000831
## Lag 2e+05 -0.007146452 -0.004570258 0.005985143
## Lag 3e+05 -0.009217947 0.007052100 -0.003528080
## Lag 4e+05 0.012452842 0.001539090 0.012019284
## Lag 5e+05 -0.003453883 -0.002526666 0.011868919
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0513807010 0.022388944 0.0390748728
## Lag 2e+05 0.0181763650 0.015057704 -0.0069757030
## Lag 3e+05 -0.0087564752 0.002449004 -0.0209181698
## Lag 4e+05 -0.0141742713 -0.018485304 0.0006895745
## Lag 5e+05 0.0003097293 -0.009659475 -0.0111137530
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.034449741 0.0379530660 0.095050344
## Lag 2e+05 -0.013672465 0.0138515683 0.011267719
## Lag 3e+05 -0.023093278 -0.0001217718 -0.007843089
## Lag 4e+05 0.006647143 0.0343560519 0.018476382
## Lag 5e+05 0.008343528 0.0231546912 -0.001560119
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.020123376 0.009856292
## Lag 2e+05 -0.003684699 -0.000271797
## Lag 3e+05 -0.012797876 -0.009949849
## Lag 4e+05 0.004640038 0.005653602
## Lag 5e+05 -0.005776410 -0.002706751
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.00000000 1.0000000000
## Lag 1e+05 0.0657338461 0.05896226 0.0105477275
## Lag 2e+05 0.0171972151 0.02660925 -0.0267843687
## Lag 3e+05 -0.0077608049 -0.01439021 0.0152895868
## Lag 4e+05 -0.0241158792 -0.02138720 0.0037343262
## Lag 5e+05 0.0005037434 0.02062716 -0.0001946632
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.080709477 0.04132275 0.042980096
## Lag 2e+05 0.014636621 0.01029496 0.004512051
## Lag 3e+05 -0.008151085 -0.02076592 -0.002276335
## Lag 4e+05 -0.017233894 -0.01551196 -0.015867643
## Lag 5e+05 0.020163499 -0.03161687 0.013709450
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0626179275 0.034816544 0.123535214
## Lag 2e+05 0.0113537309 -0.006808964 -0.007030028
## Lag 3e+05 -0.0017770588 -0.004367898 -0.001170230
## Lag 4e+05 -0.0203639856 0.025515570 0.018507845
## Lag 5e+05 0.0006252052 0.026714747 -0.013437256
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.07262593 0.016167966
## Lag 2e+05 0.00476256 0.014830420
## Lag 3e+05 -0.02010297 -0.016915644
## Lag 4e+05 -0.01965500 -0.029498841
## Lag 5e+05 -0.02012968 -0.002425949
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.042503906 0.030385894 0.032874779
## Lag 2e+05 -0.008768372 0.009791427 0.002714287
## Lag 3e+05 0.014161524 0.015147140 -0.015318507
## Lag 4e+05 -0.008794261 -0.016325846 0.006624753
## Lag 5e+05 -0.007048150 -0.014257494 -0.012600884
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.055577497 0.008382335 0.0465090591
## Lag 2e+05 0.001772942 -0.011470015 -0.0049638602
## Lag 3e+05 0.005908946 -0.002000963 0.0218300801
## Lag 4e+05 -0.025666849 -0.006282408 0.0004389731
## Lag 5e+05 0.011051621 0.003914083 -0.0060901755
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.038943233 0.030766773 0.13492348
## Lag 2e+05 0.003488907 -0.011653814 0.04222703
## Lag 3e+05 0.007107461 0.009844802 -0.03135508
## Lag 4e+05 -0.010475917 0.013332071 -0.01403413
## Lag 5e+05 0.008608570 0.001969104 -0.01278452
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.065382705 0.023754470
## Lag 2e+05 0.007962779 0.005361059
## Lag 3e+05 0.006032196 -0.006883518
## Lag 4e+05 -0.004600006 -0.004538942
## Lag 5e+05 -0.009101636 0.014811711
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.069248649 0.050574620 0.0682342611
## Lag 2e+05 0.009167513 0.014296743 0.0102546798
## Lag 3e+05 -0.001734115 -0.013963758 -0.0153888298
## Lag 4e+05 -0.008062514 0.001255802 0.0074614418
## Lag 5e+05 0.006305944 0.010205000 -0.0001554952
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.074093263 0.069292466 0.055938984
## Lag 2e+05 0.020044668 0.012040137 0.001329189
## Lag 3e+05 -0.002089091 -0.007058196 0.002667126
## Lag 4e+05 -0.027695203 -0.000974469 -0.009358984
## Lag 5e+05 -0.012043456 0.005425797 0.001181745
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.066686166 0.0405881592 0.15051345
## Lag 2e+05 0.013438472 -0.0049499299 0.02940043
## Lag 3e+05 0.005016311 -0.0054744172 -0.00188604
## Lag 4e+05 -0.004102386 -0.0008073118 -0.03164577
## Lag 5e+05 0.010017967 -0.0151543509 0.00241825
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.038573458 0.044875968
## Lag 2e+05 0.007157131 -0.001626672
## Lag 3e+05 -0.004887081 -0.002532669
## Lag 4e+05 -0.003407678 -0.016066903
## Lag 5e+05 0.013141889 0.010550886
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.044537696 0.027377121 0.0577927496
## Lag 2e+05 -0.004748132 -0.002064222 -0.0150213882
## Lag 3e+05 -0.017072765 -0.010295486 -0.0179429790
## Lag 4e+05 0.001039703 -0.009798092 0.0008094832
## Lag 5e+05 0.012070302 0.010867092 0.0010456336
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.072987293 0.07225280 0.073751171
## Lag 2e+05 0.033230119 0.01450779 0.003343774
## Lag 3e+05 0.006819024 0.02247167 -0.010229074
## Lag 4e+05 0.042658950 0.02682414 0.017181310
## Lag 5e+05 0.021667398 0.01827915 0.024783262
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.039803709 0.061090857 0.120659473
## Lag 2e+05 -0.002883166 -0.005420350 0.041173475
## Lag 3e+05 -0.006033571 -0.015765729 -0.003438104
## Lag 4e+05 -0.003624401 -0.001083903 0.008387197
## Lag 5e+05 0.006866806 -0.004956215 -0.018368581
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.036118539 0.012277492
## Lag 2e+05 -0.008256993 -0.009132051
## Lag 3e+05 -0.009410662 -0.009906133
## Lag 4e+05 -0.025474315 -0.006566419
## Lag 5e+05 -0.003627699 0.031555326
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.055074457 0.064001059 0.0996163298
## Lag 2e+05 0.008115219 0.019766539 0.0204302553
## Lag 3e+05 -0.000656337 -0.003699488 -0.0465309355
## Lag 4e+05 0.010365652 -0.022629495 -0.0002935412
## Lag 5e+05 0.008118135 0.006553284 -0.0005845760
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.102794767 0.052914096 0.039359432
## Lag 2e+05 0.015796424 0.007348315 0.005213427
## Lag 3e+05 -0.008786293 -0.021360545 0.003840813
## Lag 4e+05 -0.007208975 0.014906126 -0.009529108
## Lag 5e+05 0.004742257 0.009078032 0.012482831
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.04787745 0.05890178 0.141341827
## Lag 2e+05 0.01514419 -0.01688201 0.026910319
## Lag 3e+05 0.01467193 -0.01096304 0.009324292
## Lag 4e+05 0.02050835 -0.01755032 0.005000421
## Lag 5e+05 0.01132446 -0.01229581 0.017490699
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0396995850 0.027081604
## Lag 2e+05 -0.0076330425 0.015768020
## Lag 3e+05 0.0007369334 0.018731212
## Lag 4e+05 0.0217916656 -0.007478782
## Lag 5e+05 0.0046649778 -0.013985648
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000e+00 1.000000000 1.0000000000
## Lag 1e+05 -8.887286e-05 0.018681766 0.0127677980
## Lag 2e+05 3.485013e-02 0.022656781 0.0010647258
## Lag 3e+05 1.960510e-02 0.016262223 0.0007034834
## Lag 4e+05 2.805256e-02 0.025431251 -0.0039409024
## Lag 5e+05 7.103869e-03 0.002544349 -0.0238735122
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000e+00 1.000000000 1.000000000
## Lag 1e+05 6.012580e-02 0.009624640 0.012822093
## Lag 2e+05 1.724395e-02 -0.001331688 0.037379770
## Lag 3e+05 9.396299e-05 -0.012643483 0.041218579
## Lag 4e+05 1.443327e-02 0.025060027 -0.006414426
## Lag 5e+05 -3.063222e-02 -0.032569720 0.034914895
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.005816846 0.020814726 0.111988588
## Lag 2e+05 0.037633576 0.016749618 0.031720991
## Lag 3e+05 0.010814460 -0.003638374 0.006303549
## Lag 4e+05 0.029558581 -0.013092542 -0.025461024
## Lag 5e+05 0.018595804 -0.022566341 -0.035729556
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.005028265 -0.016345048
## Lag 2e+05 0.033864507 0.005142851
## Lag 3e+05 0.011090467 0.021271542
## Lag 4e+05 0.011800872 0.016685160
## Lag 5e+05 0.009650436 -0.003433454
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.00000000 1.0000000000 1.0000000000
## Lag 1e+05 0.06346419 0.0608135640 0.0392303412
## Lag 2e+05 0.01177384 -0.0005993105 0.0104324699
## Lag 3e+05 0.02776525 0.0043970030 0.0008603091
## Lag 4e+05 0.00186726 0.0050071462 0.0186868810
## Lag 5e+05 -0.01197034 -0.0019944427 0.0058200499
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.095898275 0.056620016 0.0564258264
## Lag 2e+05 0.001297143 0.020226772 -0.0101388756
## Lag 3e+05 -0.005374464 0.012895968 0.0245446091
## Lag 4e+05 0.016164676 -0.002138627 0.0146778528
## Lag 5e+05 -0.012980958 -0.003148926 0.0001882081
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.075081237 0.046215671 0.150032614
## Lag 2e+05 0.017224279 -0.012988403 0.033967440
## Lag 3e+05 0.025420781 0.002284640 0.013536498
## Lag 4e+05 0.004776430 -0.018727145 0.015808716
## Lag 5e+05 -0.006705578 0.006836314 0.001965943
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.049522963 0.026735695
## Lag 2e+05 -0.006286827 0.007610961
## Lag 3e+05 0.023373251 0.051712543
## Lag 4e+05 0.012994976 -0.002956634
## Lag 5e+05 -0.028680070 -0.016793964
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.8570 -1.1736 -3.2826
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8128 -0.2870 -2.4174
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -1.6804 -2.7784 0.9305
## nodematch.race..wa.O absdiff.sqrt.age
## -1.9690 -0.8940
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.063316788 0.240541012 0.001028498
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.416328648 0.774077011 0.015631598
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.092870044 0.005462603 0.352114648
## nodematch.race..wa.O absdiff.sqrt.age
## 0.048958744 0.371327035
## Joint P-value (lower = worse): 0.01131501 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.02508 -0.63061 1.52053
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.43119 -0.18362 -0.82541
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.61980 2.02574 0.83244
## nodematch.race..wa.O absdiff.sqrt.age
## -0.58511 0.02940
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.97999255 0.52829399 0.12837858
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.66632707 0.85430789 0.40914040
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.53539127 0.04279178 0.40516031
## nodematch.race..wa.O absdiff.sqrt.age
## 0.55847541 0.97654323
## Joint P-value (lower = worse): 0.2948637 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.2973 0.4312 0.9483
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.2471 -1.0634 1.7299
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.1631 1.9274 -0.2507
## nodematch.race..wa.O absdiff.sqrt.age
## 0.1838 0.6173
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.76626574 0.66635093 0.34299954
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.80486119 0.28760515 0.08364479
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.87044410 0.05392801 0.80206913
## nodematch.race..wa.O absdiff.sqrt.age
## 0.85414649 0.53700446
## Joint P-value (lower = worse): 0.2022342 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 1.14313 2.04555 0.74832
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.53350 0.37696 1.62992
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 1.43627 0.26614 -0.04452
## nodematch.race..wa.O absdiff.sqrt.age
## 2.01492 1.69873
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.25298530 0.04080063 0.45426857
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.12515297 0.70620525 0.10311755
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.15092689 0.79013170 0.96448984
## nodematch.race..wa.O absdiff.sqrt.age
## 0.04391312 0.08937016
## Joint P-value (lower = worse): 0.07807274 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.7419 0.0914 0.8502
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -2.4358 -0.9536 -1.1775
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.3590 0.8377 -1.9707
## nodematch.race..wa.O absdiff.sqrt.age
## -0.2052 0.6420
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.45814479 0.92717303 0.39522923
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.01485750 0.34029590 0.23899580
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.71961407 0.40217402 0.04875507
## nodematch.race..wa.O absdiff.sqrt.age
## 0.83742176 0.52086056
## Joint P-value (lower = worse): 0.03083343 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.869713 -1.652334 -0.625459
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.183427 -0.783863 -0.631163
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -1.198434 -1.575838 -0.005753
## nodematch.race..wa.O absdiff.sqrt.age
## -0.949194 -0.906380
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.3844571 0.0984665 0.5316698
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8544633 0.4331207 0.5279338
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.2307481 0.1150631 0.9954099
## nodematch.race..wa.O absdiff.sqrt.age
## 0.3425222 0.3647346
## Joint P-value (lower = worse): 0.6983658 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.08131 0.60608 -0.55426
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 1.21848 -2.37124 0.29835
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.31885 -0.86030 0.28316
## nodematch.race..wa.O absdiff.sqrt.age
## -0.37512 -0.26605
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.93519649 0.54446140 0.57939887
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.22304080 0.01772852 0.76543721
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.74983727 0.38962186 0.77705638
## nodematch.race..wa.O absdiff.sqrt.age
## 0.70757319 0.79020021
## Joint P-value (lower = worse): 0.2535987 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6526 1.4174 -1.2791
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.8558 -1.1092 0.9931
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.3931 -2.2010 -0.5969
## nodematch.race..wa.O absdiff.sqrt.age
## 1.6175 0.2823
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.51402403 0.15635796 0.20084651
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.39209280 0.26735687 0.32064190
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.69422969 0.02773637 0.55057427
## nodematch.race..wa.O absdiff.sqrt.age
## 0.10577329 0.77774150
## Joint P-value (lower = worse): 0.1463564 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 8
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.53103 58.749 0.33919 0.37806
## nodefactor.deg.main.1 1.35907 61.293 0.35388 0.39316
## nodefactor.race..wa.B -0.24233 19.634 0.11336 0.12626
## nodefactor.race..wa.H -0.02647 29.685 0.17139 0.21330
## nodefactor.region.EW 0.36897 31.080 0.17944 0.26167
## nodefactor.region.OW 0.07723 60.576 0.34974 0.41199
## concurrent 0.43157 52.593 0.30365 0.34138
## nodematch.race..wa.B 0.01072 2.947 0.01702 0.01891
## nodematch.race..wa.H -0.10673 7.378 0.04260 0.05951
## nodematch.race..wa.O 0.48705 44.642 0.25774 0.28298
## nodematch.region -0.13870 50.492 0.29151 0.33553
## absdiff.sqrt.age 1.16470 57.758 0.33347 0.34954
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -113.50 -39.500 0.5000 40.500 115.50
## nodefactor.deg.main.1 -117.00 -40.000 1.0000 42.000 122.00
## nodefactor.race..wa.B -38.52 -13.517 -0.5168 12.483 38.48
## nodefactor.race..wa.H -57.34 -20.340 -0.3400 19.660 58.66
## nodefactor.region.EW -59.48 -20.482 0.5180 21.518 62.52
## nodefactor.region.OW -116.59 -41.585 0.4145 40.415 119.41
## concurrent -101.00 -35.000 0.0000 35.000 104.00
## nodematch.race..wa.B -5.48 -2.480 -0.4798 1.520 6.52
## nodematch.race..wa.H -14.18 -5.181 -0.1815 4.819 14.82
## nodematch.race..wa.O -86.08 -30.081 0.9192 29.919 87.92
## nodematch.region -98.00 -34.000 0.0000 34.000 100.00
## absdiff.sqrt.age -111.85 -37.950 1.2638 40.021 114.76
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.main.1
## edges 1.00000000 0.8191600
## nodefactor.deg.main.1 0.81915999 1.0000000
## nodefactor.race..wa.B 0.41006346 0.3122276
## nodefactor.race..wa.H 0.54023564 0.4803948
## nodefactor.region.EW 0.38492428 0.3289852
## nodefactor.region.OW 0.62787011 0.5196290
## concurrent 0.95408852 0.7792915
## nodematch.race..wa.B 0.07844116 0.0561681
## nodematch.race..wa.H 0.17526505 0.1667161
## nodematch.race..wa.O 0.84654401 0.6819006
## nodematch.region 0.93264345 0.7603297
## absdiff.sqrt.age 0.84514241 0.6911694
## nodefactor.race..wa.B nodefactor.race..wa.H
## edges 0.41006346 0.54023564
## nodefactor.deg.main.1 0.31222763 0.48039476
## nodefactor.race..wa.B 1.00000000 0.18158229
## nodefactor.race..wa.H 0.18158229 1.00000000
## nodefactor.region.EW 0.09196458 0.34182415
## nodefactor.region.OW 0.22450735 0.31467849
## concurrent 0.39387702 0.52730440
## nodematch.race..wa.B 0.36229037 0.01153152
## nodematch.race..wa.H 0.01112327 0.56567664
## nodematch.race..wa.O 0.09353360 0.11972938
## nodematch.region 0.39084352 0.48432176
## absdiff.sqrt.age 0.34818579 0.45815016
## nodefactor.region.EW nodefactor.region.OW concurrent
## edges 0.384924275 0.62787011 0.95408852
## nodefactor.deg.main.1 0.328985216 0.51962904 0.77929154
## nodefactor.race..wa.B 0.091964582 0.22450735 0.39387702
## nodefactor.race..wa.H 0.341824151 0.31467849 0.52730440
## nodefactor.region.EW 1.000000000 0.10214735 0.36981952
## nodefactor.region.OW 0.102147346 1.00000000 0.60085427
## concurrent 0.369819521 0.60085427 1.00000000
## nodematch.race..wa.B 0.007274091 0.03542512 0.07467856
## nodematch.race..wa.H 0.181277479 0.09344854 0.17332120
## nodematch.race..wa.O 0.281759455 0.55425078 0.80077657
## nodematch.region 0.266151382 0.56609014 0.88956518
## absdiff.sqrt.age 0.324351949 0.53076676 0.80159787
## nodematch.race..wa.B nodematch.race..wa.H
## edges 0.0784411592 0.1752650526
## nodefactor.deg.main.1 0.0561680994 0.1667160639
## nodefactor.race..wa.B 0.3622903735 0.0111232669
## nodefactor.race..wa.H 0.0115315171 0.5656766432
## nodefactor.region.EW 0.0072740915 0.1812774787
## nodefactor.region.OW 0.0354251239 0.0934485447
## concurrent 0.0746785561 0.1733211976
## nodematch.race..wa.B 1.0000000000 -0.0004496876
## nodematch.race..wa.H -0.0004496876 1.0000000000
## nodematch.race..wa.O 0.0088735798 0.0193204716
## nodematch.region 0.0730543408 0.1517600224
## absdiff.sqrt.age 0.0652970098 0.1544967005
## nodematch.race..wa.O nodematch.region
## edges 0.84654401 0.93264345
## nodefactor.deg.main.1 0.68190058 0.76032972
## nodefactor.race..wa.B 0.09353360 0.39084352
## nodefactor.race..wa.H 0.11972938 0.48432176
## nodefactor.region.EW 0.28175946 0.26615138
## nodefactor.region.OW 0.55425078 0.56609014
## concurrent 0.80077657 0.88956518
## nodematch.race..wa.B 0.00887358 0.07305434
## nodematch.race..wa.H 0.01932047 0.15176002
## nodematch.race..wa.O 1.00000000 0.79670401
## nodematch.region 0.79670401 1.00000000
## absdiff.sqrt.age 0.71407851 0.78912696
## absdiff.sqrt.age
## edges 0.84514241
## nodefactor.deg.main.1 0.69116944
## nodefactor.race..wa.B 0.34818579
## nodefactor.race..wa.H 0.45815016
## nodefactor.region.EW 0.32435195
## nodefactor.region.OW 0.53076676
## concurrent 0.80159787
## nodematch.race..wa.B 0.06529701
## nodematch.race..wa.H 0.15449670
## nodematch.race..wa.O 0.71407851
## nodematch.region 0.78912696
## absdiff.sqrt.age 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.107201666 0.0971499174 0.108236388
## Lag 2e+05 0.014100040 0.0128416315 0.008422860
## Lag 3e+05 0.005576469 0.0140954309 -0.009216352
## Lag 4e+05 0.023332174 0.0002030943 0.007243501
## Lag 5e+05 -0.001533415 -0.0118953843 0.008824015
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.163778787 0.28669747 0.1476260234
## Lag 2e+05 0.049901266 0.13912365 0.0006437286
## Lag 3e+05 0.010562371 0.07155346 -0.0116874136
## Lag 4e+05 0.010231200 0.05985743 0.0075557524
## Lag 5e+05 0.005987186 0.03706429 0.0286062120
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.112016609 0.097713811 0.25371630
## Lag 2e+05 0.015151245 0.001842920 0.11858872
## Lag 3e+05 -0.001104045 0.006230627 0.05009370
## Lag 4e+05 0.035500574 0.012103183 0.01966305
## Lag 5e+05 -0.001028632 -0.021439497 0.01219816
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.092803380 0.122187889 0.0427827240
## Lag 2e+05 0.016195844 0.026567102 -0.0009037168
## Lag 3e+05 -0.007242533 0.008253839 0.0143826664
## Lag 4e+05 0.003622590 0.027013528 0.0181091746
## Lag 5e+05 -0.021767146 -0.003835779 -0.0087396447
## Chain 2
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.109483797 0.101251525 0.09625910
## Lag 2e+05 0.011128611 0.024476823 0.01298851
## Lag 3e+05 0.002665121 0.011285788 0.02652923
## Lag 4e+05 0.017711526 0.018968486 0.04145514
## Lag 5e+05 0.026776438 0.002272684 -0.01001557
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.16601285 0.302706621 0.127943744
## Lag 2e+05 0.03478950 0.144302685 0.021396734
## Lag 3e+05 0.01793421 0.050748301 0.001967084
## Lag 4e+05 0.02337138 0.034551100 0.008524592
## Lag 5e+05 0.02552735 0.006667197 0.023244744
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.119560309 0.089281817 0.27440042
## Lag 2e+05 0.020815618 0.051195986 0.09348248
## Lag 3e+05 -0.009261797 -0.003573604 0.06827406
## Lag 4e+05 0.028219351 -0.008546737 0.04957012
## Lag 5e+05 0.029963272 0.006160104 0.01711085
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.0000000000 1.00000000 1.00000000
## Lag 1e+05 0.1016900104 0.13409035 0.03802399
## Lag 2e+05 0.0356024484 0.02371235 -0.00821082
## Lag 3e+05 0.0129394108 0.01317740 -0.01378998
## Lag 4e+05 0.0001716103 0.01926763 0.02880678
## Lag 5e+05 0.0217029868 0.03478560 0.01483852
## Chain 3
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.126336562 0.101498897 0.125251897
## Lag 2e+05 0.041907203 0.030382937 0.031745199
## Lag 3e+05 0.009712666 0.012759350 0.007379344
## Lag 4e+05 -0.022044748 -0.008285868 0.013565618
## Lag 5e+05 0.022528713 0.032942518 0.015810078
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.16232631 0.309592242 0.161755916
## Lag 2e+05 0.07692094 0.134562530 0.045473114
## Lag 3e+05 0.06931911 0.071013693 0.033528231
## Lag 4e+05 0.04593953 0.060045219 0.006267326
## Lag 5e+05 0.02426495 -0.005653156 0.031912670
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000e+00 1.00000000 1.00000000
## Lag 1e+05 1.312637e-01 0.10203496 0.28846946
## Lag 2e+05 4.626428e-02 0.03124067 0.15734713
## Lag 3e+05 -1.666307e-05 0.01904118 0.08224872
## Lag 4e+05 -3.014642e-02 -0.02913287 0.05159424
## Lag 5e+05 1.231588e-02 -0.02779936 0.04250978
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.114618899 0.153102400 0.0694198231
## Lag 2e+05 0.026375828 0.054678148 0.0186770798
## Lag 3e+05 -0.003540117 0.012205929 0.0041772899
## Lag 4e+05 -0.009114667 -0.008560262 0.0003404308
## Lag 5e+05 0.005599510 0.026265049 0.0121723950
## Chain 4
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.120201896 0.097886242 0.106091243
## Lag 2e+05 0.014825848 0.003454529 0.024613983
## Lag 3e+05 -0.017122334 -0.042318881 0.003035207
## Lag 4e+05 -0.001079834 -0.010463660 -0.010184264
## Lag 5e+05 -0.013157262 -0.004095242 -0.004323221
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.199241992 0.27098927 0.185106145
## Lag 2e+05 0.094638140 0.13304331 0.050066961
## Lag 3e+05 0.008045886 0.06816545 0.028596250
## Lag 4e+05 0.033101716 0.02291191 0.023474329
## Lag 5e+05 0.007509799 0.01710397 -0.007892857
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.133336534 0.10022500 0.277499709
## Lag 2e+05 0.020751348 0.02279752 0.104614556
## Lag 3e+05 -0.014347763 0.01046665 0.046383356
## Lag 4e+05 -0.008386051 -0.00422081 0.028871447
## Lag 5e+05 -0.021925149 -0.01226926 0.002295727
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.074173395 0.148285040 0.070126425
## Lag 2e+05 0.016795840 0.020865496 0.007446260
## Lag 3e+05 -0.021248376 -0.001025551 -0.027151866
## Lag 4e+05 0.021954321 0.006396210 -0.005411577
## Lag 5e+05 -0.006064815 -0.001605445 -0.001306567
## Chain 5
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.081029587 0.082190838 0.131134103
## Lag 2e+05 0.040711879 0.041507052 0.021828635
## Lag 3e+05 0.001614596 0.013151039 -0.003811463
## Lag 4e+05 0.005749879 0.024894959 -0.009028895
## Lag 5e+05 -0.006182521 -0.008042022 -0.007635748
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.142935789 0.26123147 0.150433191
## Lag 2e+05 0.040383638 0.13005822 0.068204881
## Lag 3e+05 0.018518151 0.08126260 0.022882479
## Lag 4e+05 0.002141269 0.04263484 -0.008660360
## Lag 5e+05 0.019036370 0.01858960 -0.002259316
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.097263039 0.105377061 0.231216144
## Lag 2e+05 0.027271828 0.030094375 0.097421184
## Lag 3e+05 0.006747019 0.020191085 0.016413950
## Lag 4e+05 0.012507585 0.005931328 0.006130820
## Lag 5e+05 -0.005438063 0.021249095 0.002633182
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.079897323 0.112369095 0.048490587
## Lag 2e+05 0.052450702 0.037529195 0.018485322
## Lag 3e+05 0.011074115 -0.002806102 0.003823390
## Lag 4e+05 0.016225260 -0.006719477 0.002929638
## Lag 5e+05 0.003504017 -0.011123304 -0.021218138
## Chain 6
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.086454475 0.082161611 0.101181456
## Lag 2e+05 0.008039614 0.021383130 0.016329711
## Lag 3e+05 -0.026259255 -0.003044588 -0.002778010
## Lag 4e+05 0.020614632 0.017551636 0.008748552
## Lag 5e+05 -0.032935917 -0.006550458 0.008419701
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.15546598 0.24566135 0.13200727
## Lag 2e+05 0.04360704 0.11812466 0.01337538
## Lag 3e+05 0.03018067 0.06519326 -0.02959334
## Lag 4e+05 0.04449853 0.04170263 0.03152186
## Lag 5e+05 -0.02088377 0.01364193 -0.02629878
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.09475195 0.080448841 0.26200450
## Lag 2e+05 0.02045754 0.013264774 0.12525730
## Lag 3e+05 -0.01013718 0.007592905 0.06476571
## Lag 4e+05 0.01125491 -0.008119413 0.03404894
## Lag 5e+05 -0.02468385 -0.028725901 0.02232980
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.08053239 0.126961167 0.040711812
## Lag 2e+05 0.00345948 0.023130790 -0.001576178
## Lag 3e+05 -0.02683426 -0.014206564 -0.012923134
## Lag 4e+05 0.01149774 0.006422939 0.026248282
## Lag 5e+05 -0.03189219 -0.026683741 -0.037923408
## Chain 7
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.096792969 0.11113128 0.083560925
## Lag 2e+05 0.017842233 0.04733173 0.014930625
## Lag 3e+05 0.009574316 0.01501156 0.005653635
## Lag 4e+05 0.026775832 0.03083417 -0.003235266
## Lag 5e+05 0.007626324 0.02577537 0.029339061
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.181114497 0.282609336 0.14955591
## Lag 2e+05 0.044192622 0.114369516 0.03029538
## Lag 3e+05 0.016826271 0.084351198 0.03595789
## Lag 4e+05 0.007323654 0.047588068 0.05080531
## Lag 5e+05 -0.001127711 -0.007977717 -0.02016079
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.111596994 0.08726044 0.25894485
## Lag 2e+05 0.031272312 -0.01442282 0.11047496
## Lag 3e+05 0.005521659 0.01577971 0.07454468
## Lag 4e+05 0.009281784 0.01624964 0.03173569
## Lag 5e+05 0.002316377 -0.00348819 -0.00546960
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.080784324 0.112488932 0.054827097
## Lag 2e+05 0.010630917 0.018465427 0.008611661
## Lag 3e+05 0.006789876 0.007122142 -0.001841955
## Lag 4e+05 0.022172887 0.033279227 0.017149874
## Lag 5e+05 0.001537216 0.002643699 0.018909565
## Chain 8
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.077946046 0.088379488 0.122723768
## Lag 2e+05 -0.002838999 0.023338619 0.024761710
## Lag 3e+05 0.034946154 0.022071148 0.003625276
## Lag 4e+05 -0.003233809 -0.001029780 0.028942622
## Lag 5e+05 0.021136814 0.008303228 -0.015399237
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.172593860 0.27485659 0.136182880
## Lag 2e+05 0.051720138 0.14337077 0.020233661
## Lag 3e+05 -0.009865532 0.08898305 0.037647365
## Lag 4e+05 -0.026318233 0.03037949 0.009331581
## Lag 5e+05 0.015779880 0.01801809 -0.003857793
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.093028621 0.100040143 0.29085724
## Lag 2e+05 -0.002795369 0.030215289 0.13519533
## Lag 3e+05 0.039712943 0.001731396 0.07155334
## Lag 4e+05 0.001773425 0.030241291 0.04791766
## Lag 5e+05 0.025153124 -0.021444244 0.02397066
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.059384559 0.112640183 0.024909529
## Lag 2e+05 -0.002925037 0.003830332 -0.017990270
## Lag 3e+05 0.025524850 0.041435418 0.028425693
## Lag 4e+05 0.007552126 0.007105551 0.013858127
## Lag 5e+05 0.036137375 0.023169485 0.008951136
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.27203 0.43059 -0.01140
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 1.25382 1.08425 -1.12831
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.02260 -0.11004 0.48603
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.38023 0.38935 -0.06455
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.7855995 0.6667647 0.9909007
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.2099061 0.2782524 0.2591904
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.9819700 0.9123816 0.6269460
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.7037784 0.6970192 0.9485360
## Joint P-value (lower = worse): 0.9514591 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.43206 0.30302 0.90190
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.74888 0.07457 2.02414
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.14296 2.20000 -0.25170
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.86021 0.31337 0.29216
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.66570064 0.76187488 0.36711069
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.45392924 0.94055668 0.04295529
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.88631791 0.02780711 0.80127021
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.38967231 0.75400051 0.77016343
## Joint P-value (lower = worse): 0.2285956 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.3895 -0.1495 -0.3312
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.2346 -0.6836 -0.1458
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.3144 0.5468 0.4957
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -0.2068 -0.1197 0.4561
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6969024 0.8811465 0.7404566
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.8145370 0.4942003 0.8840979
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.7532402 0.5845497 0.6201312
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.8361410 0.9047596 0.6483450
## Joint P-value (lower = worse): 0.9735093 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.2518 -0.6784 -0.5240
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.6681 -1.1866 -1.1048
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -1.4056 1.2337 0.6297
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -1.1925 -1.4618 -1.2269
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.2106279 0.4975343 0.6003116
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.5040847 0.2354013 0.2692553
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.1598567 0.2173122 0.5288927
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.2330711 0.1437902 0.2198691
## Joint P-value (lower = worse): 0.4545643 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.4483 0.6562 -0.8178
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.3581 -0.5710 -1.3790
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.4832 0.6111 -0.3838
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.2026 -0.3605 0.6986
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.6539200 0.5116727 0.4134578
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.1744462 0.5680209 0.1679053
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.6289549 0.5411305 0.7011286
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.8394658 0.7184949 0.4847829
## Joint P-value (lower = worse): 0.1856638 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.19158 0.12359 -1.27909
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -0.44871 -0.47010 -0.03950
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.08826 -1.53536 -0.30877
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.32399 0.16393 0.43820
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.8480703 0.9016364 0.2008661
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.6536384 0.6382869 0.9684938
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.9296716 0.1246945 0.7574953
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.7459437 0.8697835 0.6612424
## Joint P-value (lower = worse): 0.9257134 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -1.10830 -1.04594 -0.73286
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.67201 -0.02663 0.79794
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.71822 -0.98229 0.54798
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## -1.81875 -0.99279 -1.07573
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.26773156 0.29558908 0.46364558
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.50157519 0.97875414 0.42490609
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.47262261 0.32595633 0.58370393
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.06894968 0.32081087 0.28204726
## Joint P-value (lower = worse): 0.2512177 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## -0.13507 -0.33782 -1.69603
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## -1.72321 -0.54795 -0.01553
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## -0.14909 -1.64587 -2.24098
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.72660 0.26310 0.17459
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.1 nodefactor.race..wa.B
## 0.89255988 0.73549994 0.08988060
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## 0.08485034 0.58372629 0.98760884
## concurrent nodematch.race..wa.B nodematch.race..wa.H
## 0.88148542 0.09979034 0.02502725
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## 0.46747011 0.79247398 0.86140188
## Joint P-value (lower = worse): 0.3494532 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Summary of model fit
Model 1
summary(est.p.buildup.bal[[1]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + degrange(from = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55fde6a2dd28>
##
## Iterations: 94 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -9.92021 0.02472 0 <1e-04 ***
## deg3+ -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 2
summary(est.p.buildup.bal[[2]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55fe0a9ecac0>
##
## Iterations: 81 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.06398 0.02999 0 < 1e-04 ***
## nodefactor.race..wa.B 0.24787 0.06619 0 0.000181 ***
## nodefactor.race..wa.H 0.45242 0.04836 0 < 1e-04 ***
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 3
summary(est.p.buildup.bal[[3]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55fe28b03d98>
##
## Iterations: 95 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.5478 0.1540 0 < 1e-04 ***
## nodefactor.race..wa.B 0.6644 0.1385 0 < 1e-04 ***
## nodefactor.race..wa.H 0.8716 0.1472 0 < 1e-04 ***
## nodematch.race..wa.B -0.5199 0.3776 0 0.16852
## nodematch.race..wa.H -0.2345 0.2072 0 0.25788
## nodematch.race..wa.O 0.5017 0.1558 0 0.00128 **
## deg3+ -Inf 0.0000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.0000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.0000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 4
summary(est.p.buildup.bal[[4]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodematch("race..wa", diff = TRUE) + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55fe46dd2c40>
##
## Iterations: 90 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -10.42812 0.15677 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.13633 0.03373 0 < 1e-04 ***
## nodefactor.race..wa.B 0.64962 0.13916 0 < 1e-04 ***
## nodefactor.race..wa.H 0.88359 0.14764 0 < 1e-04 ***
## nodematch.race..wa.B -0.51254 0.37728 0 0.17430
## nodematch.race..wa.H -0.23125 0.20676 0 0.26338
## nodematch.race..wa.O 0.49945 0.15667 0 0.00143 **
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 5
summary(est.p.buildup.bal[[5]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55fe651e7160>
##
## Iterations: 110 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -1.043e+01 1.581e-01 0 < 1e-04 ***
## nodefactor.deg.main.1 -1.365e-01 3.396e-02 0 < 1e-04 ***
## nodefactor.race..wa.B 6.511e-01 1.380e-01 0 < 1e-04 ***
## nodefactor.race..wa.H 8.855e-01 1.470e-01 0 < 1e-04 ***
## nodefactor.region.EW -2.614e-03 5.676e-02 0 0.96327
## nodefactor.region.OW 5.361e-04 3.685e-02 0 0.98839
## nodematch.race..wa.B -5.175e-01 3.807e-01 0 0.17396
## nodematch.race..wa.H -2.343e-01 2.070e-01 0 0.25770
## nodematch.race..wa.O 5.014e-01 1.553e-01 0 0.00124 **
## deg3+ -Inf 0.000e+00 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.000e+00 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.000e+00 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 6
summary(est.p.buildup.bal[[6]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55fe8371dfc0>
##
## Iterations: 104 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -9.8746130 0.1605361 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.1333392 0.0338123 0 < 1e-04 ***
## nodefactor.race..wa.B 0.6648410 0.1384549 0 < 1e-04 ***
## nodefactor.race..wa.H 0.8824286 0.1470090 0 < 1e-04 ***
## nodefactor.region.EW 0.0008487 0.0573078 0 0.98818
## nodefactor.region.OW -0.0007959 0.0367502 0 0.98272
## nodematch.race..wa.B -0.5239213 0.3757057 0 0.16317
## nodematch.race..wa.H -0.2337111 0.2078797 0 0.26090
## nodematch.race..wa.O 0.5016253 0.1557783 0 0.00128 **
## absdiff.sqrt.age -0.5707063 0.0327775 0 < 1e-04 ***
## deg3+ -Inf 0.0000000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.0000000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.0000000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 7
summary(est.p.buildup.bal[[7]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + concurrent +
## nodematch("race..wa", diff = TRUE) + absdiff("sqrt.age") +
## degrange(from = 3) + offset(nodematch("role.class", diff = TRUE,
## keep = 1:2))
## <environment: 0x55fea1cd7b10>
##
## Iterations: 63 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -1.169e+01 1.672e-01 0 < 1e-04 ***
## nodefactor.deg.main.1 -9.259e-02 2.851e-02 0 0.00117 **
## nodefactor.race..wa.B 5.958e-01 1.345e-01 0 < 1e-04 ***
## nodefactor.race..wa.H 7.436e-01 1.449e-01 0 < 1e-04 ***
## nodefactor.region.EW 5.817e-04 4.796e-02 0 0.99032
## nodefactor.region.OW -8.268e-04 3.104e-02 0 0.97875
## concurrent 2.503e+00 6.280e-02 0 < 1e-04 ***
## nodematch.race..wa.B -5.234e-01 3.760e-01 0 0.16397
## nodematch.race..wa.H -2.307e-01 2.072e-01 0 0.26557
## nodematch.race..wa.O 5.011e-01 1.558e-01 0 0.00130 **
## absdiff.sqrt.age -5.455e-01 3.241e-02 0 < 1e-04 ***
## deg3+ -Inf 0.000e+00 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.000e+00 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.000e+00 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Model 8
summary(est.p.buildup.bal[[8]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.main") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + concurrent +
## nodematch("race..wa", diff = TRUE) + nodematch("region",
## diff = FALSE) + absdiff("sqrt.age") + degrange(from = 3) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55feba616980>
##
## Iterations: 67 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -13.18237 0.17453 0 < 1e-04 ***
## nodefactor.deg.main.1 -0.09252 0.02857 0 0.001201 **
## nodefactor.race..wa.B 0.62960 0.13436 0 < 1e-04 ***
## nodefactor.race..wa.H 0.78694 0.14419 0 < 1e-04 ***
## nodefactor.region.EW 0.64069 0.03785 0 < 1e-04 ***
## nodefactor.region.OW 0.22781 0.02186 0 < 1e-04 ***
## concurrent 2.50387 0.06360 0 < 1e-04 ***
## nodematch.race..wa.B -0.59941 0.37661 0 0.111474
## nodematch.race..wa.H -0.33275 0.20642 0 0.106962
## nodematch.race..wa.O 0.53795 0.15579 0 0.000554 ***
## nodematch.region 1.86437 0.05768 0 < 1e-04 ***
## absdiff.sqrt.age -0.54591 0.03241 0 < 1e-04 ***
## deg3+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg3+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 31.57143
## Crude Coefficient: 3.420066
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 3.423419Network diagnostics
Model 1
(dx_pers1 <- netdx(est.p.buildup.bal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.5 2062.385 0.022 40.346
## nodefactor.deg.main.1 NA 1845.361 NA 48.510
## nodefactor.race..wa.B NA 248.598 NA 14.635
## nodefactor.race..wa.H NA 448.375 NA 20.592
## nodefactor.region.EW NA 416.428 NA 20.456
## nodefactor.region.OW NA 1353.488 NA 37.456
## concurrent NA 629.675 NA 28.457
## nodematch.race..wa.B NA 7.228 NA 2.763
## nodematch.race..wa.H NA 23.651 NA 4.575
## nodematch.race..wa.O NA 1424.092 NA 35.638
## nodematch.region NA 916.896 NA 30.829
## absdiff.sqrt.age NA 2352.860 NA 58.252
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.619 -0.030 30.094
## Pct Edges Diss 0.032 0.032 -0.002 0.004plot(dx_pers1, type="formation")
plot(dx_pers1, type="duration")
plot(dx_pers1, type="dissolution")
Model 2
(dx_pers2 <- netdx(est.p.buildup.bal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2056.056 0.019 39.921
## nodefactor.deg.main.1 NA 1854.252 NA 47.558
## nodefactor.race..wa.B 285.517 291.098 0.020 17.202
## nodefactor.race..wa.H 605.340 614.269 0.015 23.197
## nodefactor.region.EW NA 434.546 NA 21.218
## nodefactor.region.OW NA 1335.572 NA 39.994
## concurrent NA 633.857 NA 28.000
## nodematch.race..wa.B NA 10.109 NA 3.314
## nodematch.race..wa.H NA 45.909 NA 7.242
## nodematch.race..wa.O NA 1250.107 NA 31.136
## nodematch.region NA 905.326 NA 28.476
## absdiff.sqrt.age NA 2351.321 NA 58.872
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.595 -0.031 30.082
## Pct Edges Diss 0.032 0.032 -0.001 0.004plot(dx_pers2, type="formation")
plot(dx_pers2, type="duration")
plot(dx_pers2, type="dissolution")
Model 3
(dx_pers3 <- netdx(est.p.buildup.bal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2056.244 0.019 42.971
## nodefactor.deg.main.1 NA 1852.613 NA 48.895
## nodefactor.race..wa.B 285.517 288.433 0.010 17.041
## nodefactor.race..wa.H 605.340 615.979 0.018 23.869
## nodefactor.region.EW NA 436.181 NA 20.177
## nodefactor.region.OW NA 1335.686 NA 38.562
## concurrent NA 633.555 NA 28.758
## nodematch.race..wa.B 8.480 8.891 0.048 3.014
## nodematch.race..wa.H 51.181 51.955 0.015 6.664
## nodematch.race..wa.O 1247.081 1274.925 0.022 34.913
## nodematch.region NA 904.744 NA 29.157
## absdiff.sqrt.age NA 2352.026 NA 63.986
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.578 -0.031 30.065
## Pct Edges Diss 0.032 0.032 0.000 0.004plot(dx_pers3, type="formation")
plot(dx_pers3, type="duration")
plot(dx_pers3, type="dissolution")
Model 4
(dx_pers4 <- netdx(est.p.buildup.bal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2053.698 0.018 37.942
## nodefactor.deg.main.1 1699.000 1730.191 0.018 46.818
## nodefactor.race..wa.B 285.517 289.530 0.014 15.258
## nodefactor.race..wa.H 605.340 616.321 0.018 23.595
## nodefactor.region.EW NA 433.458 NA 20.344
## nodefactor.region.OW NA 1331.695 NA 36.791
## concurrent NA 633.139 NA 28.303
## nodematch.race..wa.B 8.480 8.383 -0.011 2.809
## nodematch.race..wa.H 51.181 52.227 0.020 7.220
## nodematch.race..wa.O 1247.081 1269.715 0.018 31.072
## nodematch.region NA 905.859 NA 28.228
## absdiff.sqrt.age NA 2350.070 NA 57.350
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.557 -0.032 29.952
## Pct Edges Diss 0.032 0.032 0.001 0.004plot(dx_pers4, type="formation")
plot(dx_pers4, type="duration")
plot(dx_pers4, type="dissolution")
Model 5
(dx_pers5 <- netdx(est.p.buildup.bal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2055.139 0.019 41.661
## nodefactor.deg.main.1 1699.000 1734.556 0.021 45.546
## nodefactor.race..wa.B 285.517 290.455 0.017 15.865
## nodefactor.race..wa.H 605.340 614.726 0.016 23.737
## nodefactor.region.EW 424.482 431.884 0.017 19.816
## nodefactor.region.OW 1312.585 1337.385 0.019 37.674
## concurrent NA 636.386 NA 28.390
## nodematch.race..wa.B 8.480 8.236 -0.029 2.493
## nodematch.race..wa.H 51.181 51.513 0.006 6.940
## nodematch.race..wa.O 1247.081 1271.105 0.019 32.711
## nodematch.region NA 907.201 NA 29.550
## absdiff.sqrt.age NA 2346.399 NA 59.986
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.597 -0.031 30.031
## Pct Edges Diss 0.032 0.032 -0.001 0.004plot(dx_pers5, type="formation")
plot(dx_pers5, type="duration")
plot(dx_pers5, type="dissolution")
Model 6
(dx_pers6 <- netdx(est.p.buildup.bal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2057.085 0.020 39.382
## nodefactor.deg.main.1 1699.000 1736.693 0.022 43.621
## nodefactor.race..wa.B 285.517 291.456 0.021 17.076
## nodefactor.race..wa.H 605.340 618.041 0.021 23.043
## nodefactor.region.EW 424.482 430.752 0.015 19.374
## nodefactor.region.OW 1312.585 1338.213 0.020 37.984
## concurrent NA 640.503 NA 28.348
## nodematch.race..wa.B 8.480 8.686 0.024 2.921
## nodematch.race..wa.H 51.181 52.035 0.017 7.142
## nodematch.race..wa.O 1247.081 1270.407 0.019 31.552
## nodematch.region NA 910.154 NA 28.594
## absdiff.sqrt.age 1664.841 1694.914 0.018 46.359
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.625 -0.030 30.066
## Pct Edges Diss 0.032 0.032 -0.002 0.004plot(dx_pers6, type="formation")
plot(dx_pers6, type="duration")
plot(dx_pers6, type="dissolution")
Model 7
(dx_pers7 <- netdx(est.p.buildup.bal[[7]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.p.buildup.bal[[8]]$formation, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2152.499 0.067 68.509
## nodefactor.deg.main.1 1699.000 1816.857 0.069 62.499
## nodefactor.race..wa.B 285.517 302.048 0.058 20.343
## nodefactor.race..wa.H 605.340 640.471 0.058 31.457
## nodefactor.region.EW 424.482 454.200 0.070 25.429
## nodefactor.region.OW 1312.585 1397.217 0.064 58.838
## concurrent 1384.000 1465.939 0.059 59.773
## nodematch.race..wa.B 8.480 8.940 0.054 2.873
## nodematch.race..wa.H 51.181 53.670 0.049 7.709
## nodematch.race..wa.O 1247.081 1336.646 0.072 51.447
## nodematch.region NA 950.254 NA 35.454
## absdiff.sqrt.age 1664.841 1777.393 0.068 66.065
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.543 -0.033 29.931
## Pct Edges Diss 0.032 0.032 0.000 0.004plot(dx_pers7, type="formation")
plot(dx_pers7, type="duration")
plot(dx_pers7, type="dissolution")
Model 8
(dx_pers8 <- netdx(est.p.buildup.bal[[8]], nsims = 10, nsteps = 1000, ncores = 4, set.control.stergm = control.simulate.network(MCMC.burnin.min = 1e+6, MCMC.burnin.max = 1e+6)))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2017.500 2149.819 0.066 61.549
## nodefactor.deg.main.1 1699.000 1816.503 0.069 66.089
## nodefactor.race..wa.B 285.517 301.143 0.055 20.169
## nodefactor.race..wa.H 605.340 641.056 0.059 29.771
## nodefactor.region.EW 424.482 448.157 0.056 32.088
## nodefactor.region.OW 1312.585 1401.669 0.068 69.290
## concurrent 1384.000 1463.627 0.058 54.676
## nodematch.race..wa.B 8.480 8.457 -0.003 2.744
## nodematch.race..wa.H 51.181 53.844 0.052 7.910
## nodematch.race..wa.O 1247.081 1333.778 0.070 47.333
## nodematch.region 1614.000 1722.092 0.067 51.457
## absdiff.sqrt.age 1664.841 1774.170 0.066 58.189
## deg3+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 31.571 30.552 -0.032 30.002
## Pct Edges Diss 0.032 0.032 0.000 0.004plot(dx_pers8, type="formation")
plot(dx_pers8, type="duration")
plot(dx_pers8, type="dissolution")